CN108956968B - Preparation of a kit for diagnosing a proliferative disorder - Google Patents
Preparation of a kit for diagnosing a proliferative disorder Download PDFInfo
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Abstract
The present application relates to methods of diagnosing proliferative disorders. In particular, the present application relates to methods of diagnosing and/or prognosing proliferative disorders, in particular brain cancer (e.g., glioma). In particular, the present application provides a method for conveniently detecting malignant tumors by merely assaying or analyzing blood (particularly serum). Cytokines and/or angiogenic factors in serum have been found to be surprisingly powerful in indicating the presence of brain cancer in a subject. Furthermore, spectroscopic analysis of blood samples, in particular ATR-FTIR analysis, has been shown to be surprisingly effective in generating characteristics that can be correlated with the presence, extent, severity or aggressiveness of malignant tumors in a subject.
Description
The present application is a divisional application filed on 2013, 11/14/h, with application number 201380070471.3, entitled "method for diagnosing proliferative disorders".
Technical Field
The present invention relates to methods of diagnosing and/or prognosing proliferative disorders, in particular brain cancers such as gliomas. The invention also relates to related diagnostic kits and related analytical tools (e.g., databases, computer software, etc.).
Background
Proliferative disorders such as cancer are caused by uncontrolled and unregulated cell proliferation. Such cell proliferation may lead to tumor formation in the subject of interest.
Typically, tumors, such as brain tumors, were initially identified clinically in a subject by means of a variety of well-known prescreening imaging techniques, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), X-ray and Positron Emission Tomography (PET). However, such imaging techniques are expensive to deploy in view of the high cost of the equipment itself and the human resources required to operate it. Some such imaging techniques require complex operations by highly qualified professionals, and some require time-consuming analysis before conclusions can be drawn. Moreover, such techniques rarely, if ever, distinguish between benign and malignant tumors. Thus, a final biopsy is always required to confirm malignancy or benign of a given tumor.
Biopsy requires invasive surgery to extract the relevant tissue sample. In the case of brain tumors, biopsies typically require drilling into the skull of the subject, which is a highly dangerous and skilled surgical procedure. Subjects undergoing such biopsies typically require a two to three day hospitalization after, which poses an undesirable burden of care. After a biopsy has been successfully performed, it may take a long time before the malignancy or benignancy of the relevant tumor is actually determined.
Accordingly, it is highly desirable to provide a prescreening tool that is cost-effective, requires minimal human resources and techniques to operate, and does not include time-consuming analysis. Furthermore, it would be desirable to provide prescreening techniques that facilitate relatively rapid determination of malignancy or benign of a tumor with suitably high accuracy without the disadvantages inherent in biopsy.
In the recent past, a variety of biomarkers in blood have been identifiedIdentified as a useful indicator of a particular disease. For example, cytokines, chemokines and growth factors are cell signaling proteins that mediate a range of physiological responses and are associated with a variety of diseases. Such molecules are typically detected by a bioassay or immunoassay, both of which may be time consuming in view of the fact that typically only one analyte may be analyzed at a time. However, in more recent times, magnetic bead-based multiplex assay methods designed to measure multiple cytokines, chemokines and growth factors in multiple matrices such as serum, plasma and tissue culture supernatants have become easier to use in kits, e.g., Bio-Plex ProTM(see Bio-PlexpProTMAssay Handbook-http://www.bio-rad.com/webroot/web/pdf/lsr/literature/ 10014905.pdf) And (4) obtaining. However, the complexity associated with the association of a particular biomarker with a particular disease has delayed the development of the field of medical diagnostics, and such association is currently inherently unpredictable. Furthermore, such assays still require a reasonable level of skill, and such assays also destroy the sample under investigation, making repeated assays on the same sample impossible. Verification of the results is more difficult.
In other developments in the field of medical diagnostics, recent studies have shown the potential of Infrared (IR) spectroscopy in serological analysis to distinguish myocardial infarction from other chest pain [ Petrich W, Lewandrowski KB, Muhlestein JB, Hammond MED, Januzzi JL, Lewandrowski EL, Pearson RR, Olenko B, Fruh J, Haass M, hirsch MM, Kohler W, Mischler R, Mocks J, Ordonez-Llanos J, Quarder O, Somorjair, Staib a, Sylven C, Werner G, rbcack R analysts, 134(6), 2009; 1092-1098]. Spectral diagnostic methods such as these can be highly desirable for clinicians and patients if they can be clinically viable, as they potentially provide a non-destructive, rapid, cost-effective, easy-to-operate point-of-care condition diagnosis. However, it now appears that the applicability of such spectroscopic diagnostic techniques is somewhat limited in some areas, given their questionable reliability in the face of sample variance.
Disclosure of Invention
Accordingly, an object of the present invention is to solve at least one of the problems inherent in the prior art. Another object is to provide a simple, reliable and cost-effective point-of-care diagnostic method which requires minimal human resources and techniques to operate, which is not time consuming, and which facilitates a fast determination of malignancy/benign of a tumor with a suitably high accuracy.
According to a first aspect of the present invention there is provided a method of diagnosing and/or prognosing a brain cancer in a subject, the method comprising assaying a blood sample (or a component thereof) of the subject in respect of one or more (suitably pre-specified) cytokines and/or angiogenic factors.
According to a second aspect of the present invention there is provided a method of diagnosing and/or prognosing a proliferative disorder in a subject, the method comprising performing spectroscopic analysis on a blood sample (or component thereof) of the subject to generate a spectroscopic signature characteristic of the blood sample (or component thereof).
According to a third aspect of the present invention there is provided a method of detecting cancer cells in a subject, the method comprising the steps of the method of diagnosing and/or prognosing a brain cancer or proliferative disorder of the first or second aspect.
According to a fourth aspect of the present invention there is provided a method of diagnosing whether a tumour, suitably a brain tumour, such as a glioma, is malignant or benign, the method comprising the steps of the method of diagnosing and/or prognosing a brain cancer or proliferative disorder of the first or second aspect.
According to a fifth aspect of the present invention there is provided a method of monitoring the responsiveness of a subject to surgery or therapeutic treatment of a proliferative disorder, the method comprising the steps of the method of diagnosing and/or prognosing a brain cancer or proliferative disorder of the first or second aspect.
According to a sixth aspect of the present invention there is provided a diagnostic kit for diagnosing and/or prognosing brain cancer in a subject, the diagnostic kit comprising a device configured to receive a blood sample (or a component thereof) from a subject and to assay the blood sample (or a component thereof) for one or more (suitably pre-specified) cytokines and/or angiogenic factors; and means (optionally, the same as previously mentioned) for correlating or contributing to the correlation of the amount of the one or more cytokines and/or angiogenic factors in the blood sample (or component thereof) with a favorable or unfavorable diagnostic and/or prognostic outcome.
According to a seventh aspect of the present invention there is provided a diagnostic kit for diagnosing and/or prognosing a proliferative disorder in a subject, the diagnostic kit comprising a device configured to receive a blood sample (or component thereof) from a subject and perform spectroscopic analysis on the subject's blood sample (or component thereof) to generate a spectral signature characteristic of the blood sample (or component thereof); and means (optionally, the same as mentioned above) for correlating or contributing to the correlation of the spectral feature characteristic of the blood sample (or component thereof) with a favorable or unfavorable diagnostic and/or prognostic outcome.
According to an eighth aspect of the present invention there is provided use of data from a blood sample (or a component thereof) of a subject assayed for one or more (suitably pre-specified) cytokines and/or angiogenic factors to determine a favorable or unfavorable diagnostic and/or prognostic outcome of a brain cancer in the subject.
According to a ninth aspect of the present invention there is provided the use of a spectral signature of a blood sample (or a component thereof) of a subject for determining a favorable or unfavorable diagnostic and/or prognostic outcome of a proliferative disorder in the subject.
According to a tenth aspect of the present invention, there is provided a database comprising a plurality of data sets, each set relating to one or more cytokines and/or angiogenic factors in a particular blood sample (or component thereof) of a particular subject, each set being associated with a favorable or unfavorable diagnostic and/or prognostic outcome relating to brain cancer in the particular subject.
According to an eleventh aspect of the present invention there is provided a database comprising a plurality of spectral features, each feature relating to a particular blood sample (or component thereof) of a particular subject, each feature being associated with a favorable or unfavorable diagnostic and/or prognostic outcome relating to a proliferative disorder in the particular subject.
According to a twelfth aspect of the present invention, there is provided a computer readable medium (e.g. an optical disc) comprising a database as defined herein.
According to a thirteenth aspect of the present invention, there is provided a computer having installed thereon diagnostic computer software configured to operate the computer to perform predictive diagnosis and/or prognosis relating to a proliferative disorder based on spectral characteristics of a blood sample of a subject.
According to a fourteenth aspect of the present invention there is provided a computer readable medium containing diagnostic computer software as defined herein.
Suitably, the proliferative disorder is a cancer, suitably a human cancer, suitably a brain cancer (and/or associated tumor).
Features associated with one aspect of the invention (including optional, suitable and preferred features) may also be features associated with any other aspect of the invention (including optional, suitable and preferred features).
Specifically, the present application provides the following:
Item 7. the method of any one of items 4 to 6, wherein the blood sample membrane has a substantially uniform thickness within a tolerance of +/-40 μ ι η or less.
Item 17. the method of any one of items 15 or 16, wherein the cytokine and/or angiogenic factor analyte comprises a peptide selected from the group consisting of IL-1 β, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12(p70), IL-13, IL-15, IL-17, Exotaxin, basic FGF, G-CSF, GM-CSF, IFN- γ, IP-10, (MCAF), MIP-1 α, MIP-1 β, PDGF-BB, RANTES, TNF- α, VEGF, IL-1 α, IL-2R α, IL-3, IL-12(p40), IL-16, IL-18, CTACK, GRO- α, HGF, ICAM-1, and/or a mixture thereof, IFN- α 2, LIF, MCP-3, M-CSF, MIF, MIG, β -NGF, SCF, SCGF- β, SDF-1 α, TNF- β, TRAIL, VCAM-1, or a human cytokine and/or angiogenic factor selected from PDGF-AA, sHER2neu, sIL-6 Ra, prolactin, sVEGFR1, IGFBP-1, IL-18, PAI-1, VEGF C; or a mouse cytokine and/or an angiogenic factor selected from the group consisting of IL-1 α, IL-1 β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12(p40), IL-12(p70), IL-13, IL-17, Exotaxin, G-CSF, GM-CSF, IFN- γ, KC, MCP-1(MCAF), MIP-1 α, MIP-1 β, RANTES, TNF- α, IL-15, IL-18, basic FGF, LIF, M-CSF, MIG, MIP-2, PDGF-BB, VEGF.
Item 18. the method of any one of items 15 to 17, wherein the cytokine and/or angiogenic factor analyte is selected from IL-8, IL-10, IFN- γ, PDGF-BB, HGF, follistatin, angiogenin, leptin, PECAM-1, or from PDGF-AA, she 2neu, sIL-6 ra, prolactin, sVEGFR1, G-CSF, FGF.
Item 19. the method of any one of items 15 to 18, wherein the cytokine and/or angiogenic factor analyte is selected from IL-8, PDGF-BB, HGF, follistatin, angiogenin, leptin, PECAM-1, or from PDGF-AA, smer 2neu, sIL-6 ra, prolactin, sVEGFR1, G-CSF, FGF.
Item 21. the method of item 20, wherein correlating the analysis results with favorable or unfavorable diagnosis results and/or prognosis results comprises an initial comparison of the analysis results to reference criteria or to previous analysis results that have been pre-correlated with favorable or unfavorable diagnosis results and/or prognosis results (e.g., pre-correlated analysis results stored in a database).
Item 22 the method of any one of items 15 to 21, wherein the blood sample is determined with an immunoassay.
Item 23. the method of any one of items 15 to 22, wherein the blood sample is assayed using a magnetic bead-based multiplex assay designed to measure a plurality of cytokines and/or angiogenic factors.
Item 24. the method of item 23, wherein the assay suitably uses multiple fluorescently stained beads to simultaneously detect multiple cytokines and/or angiogenic factors in a single assay (e.g., a single well).
An item 26. a diagnostic kit for diagnosing and/or prognosing a proliferative disorder in a subject, the diagnostic kit comprising a device configured to receive a blood sample (or component thereof) from the subject and perform a spectroscopic analysis on the blood sample (or component thereof) of the subject to generate a spectroscopic signature characteristic of the blood sample (or component thereof); and a means (optionally the same as the means configured to receive a blood sample) to correlate or facilitate correlation of the spectral signature of the blood sample (or component thereof) with a favorable or unfavorable diagnostic and/or prognostic outcome; wherein the spectral analysis is attenuated total reflectance FTIR (ATR-FTIR), wherein during IR analysis an "ATR crystal" supports the blood sample.
A diagnostic kit for diagnosing and/or prognosing brain cancer in a subject, the diagnostic kit comprising a device configured to receive a blood sample (or a component thereof) from the subject and to assay the blood sample (or a component thereof) for one or more (suitably pre-specified) cytokines and/or angiogenic factors; and a means (optionally, the same as the means configured to receive a blood sample) for correlating or facilitating correlation of the amount of the one or more cytokines and/or angiogenic factors in the blood sample (or component thereof) with a favorable or unfavorable diagnostic and/or prognostic outcome.
Item 28. the diagnostic kit of any one of items 26 to 27, wherein the device for assaying or analyzing the blood sample is the same as the device for correlating or contributing to the correlation of the results.
Item 29. the diagnostic kit of any one of items 26 to 28, wherein the correlating means comprises or is in communication with a computer having installed thereon diagnostic computer software configured to operate the computer to perform predictive diagnosis and/or prognosis related to a proliferative disorder based on the spectral characteristics of a blood sample of a subject.
Item 31. a method or diagnostic kit substantially as hereinbefore described with reference to the examples and figures.
Drawings
Embodiments of the invention are described further below with reference to the accompanying drawings, in which:
FIGS. 1 to 7 show graphical representations of the "control mean" (light grey) and "glioma mean" (dark grey) and error bars for IL-8, angiogenin, follistatin, HGF, leptin, PDGF-BB, and PECAM-1, respectively.
FIGS. 7A to 7F show graphical representations of "control mean" (dark grey-left), "low grade glioma mean" (light grey-middle) and "high grade glioma mean" (middle grey-right) and error bars for FGF, G-CSF, sHER2neu, sIL-6R α, prolactin and sVEGFR1, respectively.
FIG. 8 is a scatter-plot correlation plot (scatter-graphical correlationchart) of PECAM-1 and PDGF-BB showing the relationship between PECAM-1 and PDGF-BB levels in 50 glioma patients and showing the degree of linearity and a correlation coefficient of 0.45.
Figures 8A-8G show a comparison of camera immunohistochemistry between gliomas and non-cancerous brain tissue, namely: A) glioma tumor sections showing 40-fold magnification of positively stained and unstained tumor cells; B) a glioma tumor section showing 40-fold magnification of negatively stained blood vessels; C) non-cancerous brain tissue at 40-fold magnification showing negatively stained blood vessels; D) a glioma tumor section showing 40-fold magnification of interstitial staining; E) glioma tumor sections showing 40-fold magnification of interstitial staining, particularly axonal bundle staining; F) non-cancerous brain tissue at 40-fold magnification showing negatively stained blood vessels; G) positively cytoplasmic stained choroid plexus tissue is shown.
FIG. 9 shows a white light interference pattern of the membrane of serum sample 1 (whole serum).
FIG. 10 shows a white light interference pattern of the membrane of serum sample 3 (serum with components above 10kDa removed).
FIG. 11 shows a representative sample of FTIR spectral characteristics for each of serum sample types 1-4.
Fig. 12 (taken from Filik J, frog MD, et al, Analyst,2012, 137, 853) shows superimposed FTIR spectral features of samples of Bovine Serum Albumin (BSA) at different average membrane thicknesses on ATR crystals.
FIG. 13 (taken from Goormightigh E, et al, Biochimica et Biophysica Acta,1999, 1422, 105) is a graph showing a) the presence in a serum sample of two characteristic amides, amide I (1650 cm)-1) And amide II (1550 cm)-1) How the area ratio of (a) varies with the BSA film thickness; and b) amide I (1650 cm)-1) And TSPA internal Standard (835 cm)-1) Is a graphical representation of how the area ratio of (a) varies with BSA film thickness.
Fig. 14 shows various superimposed spectral signatures of human whole serum dried at room temperature for 0, 2, 4, 6, 8, 16 and 32 minutes.
Fig. 15 is a graph illustrating the training set accuracy of the whole serum predictive model when the predictive model is used to evaluate the "blind set".
Fig. 16 is a graph illustrating the training set accuracy of the serotype 3 predictive model when evaluating the relevant "blind set" against the predictive model.
Figure 17 shows 0.5ml of serum in a centrifugal filter (left) and it is centrifuged such that the filter retains all serum components above the kilodalton range (100, 10 or 3kDa), allowing only serum filtrates containing components below the maximum range to pass.
Fig. 18 shows various superimposed ATR-FTIR spectral features of human whole serum dried at room temperature for 0, 2, 4, 6, 8, 16 and 32 minutes. The spectra have been offset (offset) for easy visualization.
FIGS. 19A-D show (A) the whole serum spectrum (900--1) And (B) fingerprint region (900--1) (C) pretreatedWhole serum spectra and (D) raw and unprocessed spectral data of the pre-processed fingerprint region. Variable CO2Zone (2300--1) Has been removed.
Detailed Description
Definition of
Unless otherwise indicated, the following terms used in the specification and the item book have the following meanings set forth below.
Herein, "diagnosing" or "predicting" generally includes determining the presence, extent, severity, and/or aggressiveness of a brain cancer or proliferative disorder. Thus, determining a favorable or unfavorable diagnostic or prognostic outcome generally includes determining the presence, extent, severity, and/or aggressiveness of a brain cancer or proliferative disorder. In particular embodiments, "diagnosis" or "prognosis" may refer to the mere presence of brain cancer or a proliferative disorder.
Herein, reference to a "blood sample" includes a sample of whole blood or a component thereof (e.g., serum or plasma).
Herein, "plasma" refers to the yellowish/yellowish liquid component of blood that contains blood cells in whole blood, typically in the form of a suspension. It constitutes about 55% of the total blood volume. It is the intravascular fluid portion of extracellular fluid (all extracellular fluids). It is mostly water (93% by volume) and contains dissolved proteins (major proteins are fibrinogen, globulin and albumin), glucose, coagulation factors, mineral ions (Na)+、Ca++、Mg++、HCO3 -、Cl-Etc.), hormones and carbon dioxide (plasma is the primary medium for transport of excreta products). It is noted that for plasma samples, both EDTA plasma and citrate plasma are suitable, while heparin plasma is less preferred, as such plasma may absorb certain cytokines.
Herein, "serum" refers to a component that is neither blood cells (serum does not contain leukocytes or erythrocytes) nor coagulation factors; it is plasma from which fibrinogen is removed.
"cytokines" are well known in the art as cellular signaling protein molecules secreted by a large number of cells and are a widely used class of signaling molecules in intercellular communication. Cytokines can be classified as proteins, peptides or glycoproteins; the term "cytokine" includes large and diverse families of regulatory factors produced throughout the body by cells of different embryonic origins. Some "cytokines" may also be considered "angiogenic factors" and vice versa.
"angiogenic factors" are well known in the art as angiogenic factors. In the context of the present invention, considering the combined effect of "cytokines" and "angiogenic factors" as biomarkers of proliferative disorders, they are generally considered jointly, as demonstrated in the examples and throughout the specification.
Herein, reference to "assay" or "assaying" includes any form of analysis, including standard biological assays (e.g., bioassays, immunoassays, etc.) and even spectroscopic analysis. In particular embodiments, the assay does not involve spectroscopic analysis.
As used herein, "subject" refers to an animal, preferably a mammal. In a preferred embodiment, the subject is a human subject. In other embodiments, the subject is a non-human mammal, including but not limited to a dog, cat, horse, and the like.
Throughout the description and claims of this specification, the words "comprise" and variations of the words "comprise" and "comprising" mean "including but not limited to", and they are not intended to (and do not) exclude other moieties, additives, components, integers or steps. Throughout the description and claims of this specification, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise.
Features, integers, characteristics, compounds, chemical moieties or groups described in conjunction with a particular aspect, embodiment or example of the invention are to be understood to be applicable to any other aspect, embodiment or example described herein unless incompatible therewith. All of the features disclosed in this specification (including any accompanying clause, abstract and drawings), and/or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive. The invention is not limited by the details of any of the foregoing embodiments. The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying clause, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.
The reader's attention is directed to all papers and documents which are filed concurrently with or previous to this specification in connection with this application and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference.
For the avoidance of doubt, it is stated herein that the information disclosed hereinbefore under the heading "background" in this specification is relevant to the invention and is to be understood as part of the disclosure of the invention.
As used herein, "peptide" and "protein" may be used interchangeably and mean at least two covalently attached amino acids linked by a peptide bond. The term protein encompasses purified natural products or products that may be partially or fully prepared using recombinant or synthetic techniques. The terms peptide and protein may refer to aggregates of proteins, such as dimers or other multimers; a fusion protein; a protein variant or derivative thereof. The term also includes modified forms of the protein, such as proteins modified by glycosylation, acetylation, phosphorylation, pegylation, ubiquitination, and the like. A protein may comprise amino acids that are not encoded by nucleotide codons.
"protein modification" or "protein mutation" means an amino acid substitution, insertion, and/or deletion in a polypeptide sequence, or an alteration of a moiety chemically linked to a protein. For example, the modification may be an altered carbohydrate or PEG structure attached to the protein. The proteins of the invention may comprise at least one such protein modification.
Conservative substitutions: one or more amino acid substitutions (e.g., 1,2, 5, or 10 residues) are made at amino acid residues with similar biochemical properties. In general, conservative substitutions have little to no effect on the activity of the resulting polypeptide. For example, a conservative substitution in a contact phase factor (contact phase factor) inhibitory peptide may be an amino acid substitution or combination of amino acid substitutions that do not substantially affect the ability of the peptide to inhibit the contact phase factor.
Substituted variants are those in which at least one residue in the amino acid sequence has been removed and a different residue inserted in its place. Examples of amino acids that can be substituted for the initial amino acid in a protein and are considered conservative substitutions include: ser for Ala; lys for Arg; gln or His substituted Asn; glu for Asp; asn for Gln; asp for Glu; pro for Gly; asn or Gln substituted His; leu or Val substituted Ile; ile or Val for Leu; arg or Gln for Lys; leu or Ile for Met; substitution of Met, Leu or Tyr for Phe; thr for Ser; ser is substituted for Thr; tyr replaces Trp; trp or Phe substituted Tyr; and Ile or Leu for Val.
In one embodiment, the substitutions may be in Ala, Val Leu and Ile; in Ser and Thr; in Asp and Glu; among Asn and Gln; in Lys and Arg; and/or in Phe and Tyr.
Additional information on conservative substitutions in other positions can be found in Ben-Bassat et al (J.Bacteriol.169:751-7,1987), O' Regan et al (Gene 77:237-51,1989), Sahin-Toth et al (Protein Sci.3:240-7,1994), Hochuli et al (Bio/Technology 6: 1321-5,1988), WO 00/67796(Curd et al) and standard texts available in genetics and molecular biology.
The term "modified protein" or "mutated protein" includes proteins having at least one substitution, insertion and/or deletion of an amino acid. The modified or mutated protein may have 1,2, 3, 4, 5, 6, 7, 8, 9, or 10 or more amino acid modifications (selected from substitutions, insertions, deletions and combinations thereof).
Functionally equivalent: have equivalent functions. In the case of the contact phase factor inhibitory peptide, functionally equivalent molecules include different molecules that retain the function of inhibiting the same contact phase factor. For example, functional equivalents may be provided by sequence alterations in the contact phase factor inhibitory peptides, wherein peptides having one or more sequence alterations retain the ability of the unaltered peptide to inhibit one or more contact phase factors.
Examples of sequence alterations include, but are not limited to, conservative substitutions, deletions, mutations, and insertions. In one example, a given polypeptide binds to one active, and functionally equivalent is a polypeptide that binds to the same active. Functional equivalents thus include peptides which have the same binding specificity as the polypeptide and which may be used in place of the polypeptide. In one example, functional equivalents include polypeptides in which the binding sequence is discontinuous, wherein the active binds a linear epitope.
Purification of: the term purified does not require absolute purity; rather, it is intended as a relative term. Thus, for example, a purified peptide preparation is one in which the peptide or protein is more enriched in its intracellular environment than the peptide or protein, such that the peptide is substantially separated from cellular components (nucleic acids, lipids, carbohydrates, and other polypeptides) that may accompany it. In another example, a purified peptide preparation is one in which the peptide is substantially free of contaminants, such as those that may be present after chemical synthesis of the peptide.
The present invention relates to proteins and peptides (e.g., cytokines and/or angiogenic factors) that are at least 75%, at least 80%, at least 85%, at least 90%, at least 95% identical to a protein or peptide of the present disclosure, e.g., 96% or more, 97% or more, 98% or more, or 99% or more; such proteins may have the activity of the corresponding protein or peptide of the present disclosure.
Those skilled in the art will appreciate that these ranges of sequence identity are provided for guidance only; it is possible that very important homologues falling outside the provided range can be obtained. An alternative (and not necessarily cumulative) meaning that two amino acid sequences are substantially identical is that the polypeptide of the first sequence is immunologically cross-reactive with the polypeptide of the second sequence.
Variant, fragment or fusion protein: the disclosed proteins include variants, fragments, and fusions thereof.
General methodology
The present invention provides a method for conveniently detecting malignant tumors, particularly cancerous brain tumors, by measuring/analyzing blood (particularly serum) alone. The inventors have made the surprising finding that: cytokines and/or angiogenic factors in serum indicate the presence of brain cancer. The inventors have also found that: with sufficient care in preparing the sample, spectroscopic analysis of a blood sample from a subject can yield a characteristic that can be correlated with the presence, extent, severity, or aggressiveness of a proliferative disorder, particularly a malignancy, in the subject with high accuracy.
As shown in the examples, the data provided herein supports the following perspectives: cytokines and/or angiogenic factors in a blood sample may be indicative of a brain cancer in a subject from which the blood sample was taken, in particular indicative of a brain cancer such as a glioma. Furthermore, the data provided herein supports the following notions: the spectral characteristics of the blood sample can be used to provide a rapid diagnosis and/or prediction of a proliferative disorder in a subject from which the blood sample was taken. It is reasonable to expect that the diagnostic methods of the present invention will be applicable to a wide variety of proliferative disorders, particularly a variety of brain cancers. Furthermore, based on the findings summarized in this disclosure, diagnostic methods and kits can be readily produced using conventional plant technology (route work technique) known in the art, along with any relevant diagnostic tools (e.g., software, etc.).
The present invention provides a simple, reliable and cost-effective point-of-care diagnostic method that requires minimal human resources or techniques to operate, is not time consuming and that facilitates rapid determination of malignancy/benign of a tumor with reasonably high accuracy. For example, the exemplary ATR-FTIR diagnostic method provides a diagnostic result within 10 minutes, while the cytokine/angiogenic factor assay provides a diagnostic result within 5 hours. This is a considerable contribution to the art. It is envisaged that cytokine/angiogenic factor assays may be combined with spectroscopic analysis to provide a rapid and reliable diagnosis of proliferative disorders, particularly brain cancers such as gliomas.
The methods of the invention are useful to enable a clinician to make decisions regarding the best course of treatment for a subject suffering from or suspected of developing cancer. Preferably, the diagnostic method is used to enable a clinician to decide how to treat a subject suffering from cancer. Furthermore, the method is useful for the clinician as it allows him or her to monitor the efficacy of a putative treatment for cancer. Thus, the diagnostic kit according to the present invention can be used to provide predictive information about the condition of a cancer patient so that a clinician can administer a treatment. The kit may also be used to monitor the efficacy of putative treatments for cancer. Thus, the methods and kits are very useful for guiding a clinician's cancer treatment regimen and monitoring the efficacy of such treatment regimen. Advantageously, the levels of cytokines and/or angiogenic factors in the blood can be used as diagnostic and/or prognostic markers for a variety of cancer conditions, but in particular brain cancers such as gliomas. The methods of the invention are also applicable to pre-cancerous conditions and cancers caused by oncogenic viruses.
Proliferative disorders
The proliferative disorder is suitably a cancer, suitably a cancer of the brain or spine, most suitably a brain cancer (and/or associated tumor). In a particular embodiment, the brain cancer is a glioma.
The three major types of malignant gliomas are astrocytomas, ependymomas, and oligodendrogliomas. The diagnostic method of the present invention can be used for all these types of gliomas. Mixed tumors with histological features present in the main three are called mixed gliomas, and the present invention can also be used for their diagnosis. The table below shows subtypes for high and low grade gliomas.
In particular embodiments, the brain cancer is a low-grade glioma or a high-grade glioma. In a particular embodiment, the brain cancer is any one of the hairy cell astrocytoma, oligodendroglioma, astrocytoma, anaplastic astrocytoma, oligodendroglioma, glioblastoma multiforme glioma subtypes.
In a particular embodiment, the brain cancer is a grade III glioma or a grade IV glioma.
Subject (patient)
The subject is suitably an animal, preferably a mammal. In a preferred embodiment, the subject is a human subject. In other embodiments, the subject is a non-human mammal, including but not limited to a dog, cat, horse, and the like.
The subject suitably has or is suspected of having a brain cancer or a proliferative disorder as defined herein. In particular embodiments, the subject has or is suspected of having a brain cancer (particularly a glioma).
The subject is suitably a patient with glioblastoma or gliosarcoma. In a particular embodiment, the subject is a glioblastoma patient.
Blood sample
A blood sample is suitably obtained by first drawing blood from the subject of interest. Preferably, the blood is then further processed, suitably to obtain components thereof (e.g. serum).
The blood sample (or component thereof) used in the method of the invention is suitably serum or plasma. In a particular embodiment, the blood sample is serum. In a particular embodiment, the blood sample is human serum.
Serum is suitably obtained from a blood sample of the subject of interest by methods well known in the art.
In a particular embodiment, the serum used is whole serum, most preferably human whole serum. Whole serum can be used directly for relevant assays, especially spectroscopic analysis. Alternatively, the serum sample may be diluted according to the spectral requirements (e.g., sensitivity) and the desired homogeneity of the sample being analyzed.
In another embodiment, the serum used is centrifuged serum from which molecules above a certain molecular weight have been removed. For example, serum may be centrifuged to remove components having a molecular weight above 100kDa (kilodaltons). In another embodiment, the serum may be centrifuged to remove components having a molecular weight above 10 kDa. In another embodiment, the serum may be centrifuged to remove components having a molecular weight above 3 kDa. Any or all of the above mentioned centrifuged sera can be used directly in the relevant assay, especially in spectroscopic analysis. Alternatively, the centrifuged serum sample can be diluted according to the spectral requirements (e.g., sensitivity) and the desired homogeneity of the sample being analyzed.
In case serum (suitably whole serum) is used for the immunoassay and/or spectroscopic analysis, the serum sample is suitably prepared by: the drawn blood sample is first allowed to clot suitably at room temperature, suitably for between 25 minutes and 1 hour 10 minutes. The serum is then suitably centrifuged or filtered to remove precipitated sample. The centrifugation is suitably between 9000 and 20000rpm, suitably between 10000 and 15000rpm, suitably for 5-20min, suitably performed at 2-8 ℃. Filtration of the serum sample suitably comprises filtration through a 0.8/0.22 μm duplex filter to prevent clogging of the apparatus. The serum should then be measured immediately or otherwise aliquoted and the serum samples stored in separate aliquots at-70 ℃. Prior to the assay, the serum sample is suitably diluted with a suitable sample diluent. Suitably, 1 volume of serum sample may be diluted with 2-5 volumes of sample diluent, suitably 3 volumes of sample diluent. Because physiological levels of VCAM-1 and ICAM-1 are typically found at much higher concentrations, a 1:100 sample dilution is typically required to achieve concentrations within the measurable range of the standard curve. Thus, one can optionally dilute the serum 1:50 or 1:100 as follows: 1) serum was diluted 1:4 in sample diluent and 2) further diluted with standard diluent 1: 25.
Cytokines and angiogenic factors
The present invention may suitably comprise detecting the amount (or presence) of one or more cytokines and/or angiogenic factors in a blood sample (or component thereof). Thus, cytokines and/or angiogenic factors may be used as analytes in the blood sample. The following surprising findings of the present inventors now enable the diagnosis and/or prognosis of a large number of brain cancers: a correlation between brain cancer and the amount of cytokines and/or angiogenic factors in their blood in a subject. The discovery of cytokines and/or angiogenic factors in the blood of a subject for use as biomarkers for brain cancer (particularly brain cancer such as glioma) is a significant advance in the field of medical diagnosis as it overcomes many of the problems associated with existing diagnostic methods and allows for rapid transmission of diagnosis regarding malignancies, such as malignant brain tumors.
Suitably, the analyte (i.e. cytokine and/or angiogenic factor) is predetermined. In a preferred embodiment, the analyte is a human cytokine and/or a human angiogenic factor.
In particular embodiments of the invention, the cytokine and/or angiogenic factor analyte comprises a member selected from the group consisting of IL-1 β, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12(p70), IL-13, IL-15, IL-17, Exotaxin, basic FGF, G-CSF, GM-CSF, IFN- γ, IP-10, (MCAF), MIP-1 α, MIP-1 β, BB, RANTES, TNF- α, VEGF, IL-1 α, IL-2R α, IL-3, IL-12(p40), IL-16, IL-18, CTACK, GRO- α, HGF, ICAM-1, IFN- α 2, PDGF- α 2, PDGF, and combinations thereof, LIF, MCP-3, M-CSF, MIF, MIG, β -NGF, SCF, SCGF- β, SDF-1 α, TNF- β, TRAIL, VCAM-1, or a human cytokine and/or angiogenic factor selected from PDGF-AA, sHER2neu, sIL-6R α, prolactin, sVEGFR1, IGFBP-1, IL-18, PAI-1, VEGFC; or a mouse cytokine and/or an angiogenic factor selected from the group consisting of IL-1 α, IL-1 β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12(p40), IL-12(p70), IL-13, IL-17, Exotaxin, G-CSF, GM-CSF, IFN- γ, KC, MCP-1(MCAF), MIP-1 α, MIP-1 β, RANTES, TNF- α, IL-15, IL-18, basic FGF, LIF, M-CSF, MIG, MIP-2, PDGF-BB, VEGF.
In particular embodiments of the invention, the cytokine and/or angiogenic factor analyte comprises a member selected from the group consisting of IL-1 β, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12(p70), IL-13, IL-15, IL-17, Exotaxin, basic FGF, G-CSF, GM-CSF, IFN- γ, IP-10, (MCAF), MIP-1 α, MIP-1 β, BB, RANTES, TNF- α, VEGF, IL-1 α, IL-2R α, IL-3, IL-12(p40), IL-16, IL-18, CTACK, GRO- α, HGF, ICAM-1, IFN- α 2, PDGF- α 2, PDGF, and combinations thereof, Human cytokines and/or angiogenic factors of LIF, MCP-3, M-CSF, MIF, MIG, β -NGF, SCF, SCGF- β, SDF-1 α, TNF- β, TRAIL and VCAM-1; or a mouse cytokine and/or an angiogenic factor selected from the group consisting of IL-1 α, IL-1 β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12(p40), IL-12(p70), IL-13, IL-17, Exotaxin, G-CSF, GM-CSF, IFN- γ, KC, MCP-1(MCAF), MIP-1 α, MIP-1 β, RANTES, TNF- α, IL-15, IL-18, basic FGF, LIF, M-CSF, MIG, MIP-2, PDGF-BB, VEGF.
In particular embodiments of the invention, the cytokine and/or angiogenic factor analyte comprises a member selected from the group consisting of IL-1 β, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12(p70), IL-13, IL-15, IL-17, Exotaxin, basic FGF, G-CSF, GM-CSF, IFN- γ, IP-10, (MCAF), MIP-1 α, MIP-1 β, BB, RANTES, TNF- α, VEGF, IL-1 α, IL-2R α, IL-3, IL-12(p40), IL-16, IL-18, CTACK, GRO- α, HGF, ICAM-1, IFN- α 2, LIF, PDGF-1, ICAM-1, PDGF- α 2, LIF, and combinations thereof, MCP-3, M-CSF, MIF, MIG, β -NGF, SCF, SCGF- β, SDF-1 α, TNF- β, TRAIL, VCAM-1, or a human cytokine and/or angiogenic factor selected from PDGF-AA, sHER2neu, sIL-6 Ra, prolactin, sVEGFR1, IGFBP-1, IL-18, PAI-1, VEGFC.
In particular embodiments of the invention, the cytokine and/or angiogenic factor analyte comprises a member selected from the group consisting of IL-1 β, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12(p70), IL-13, IL-15, IL-17, Exotaxin, basic FGF, G-CSF, GM-CSF, IFN- γ, IP-10, (MCAF), MIP-1 α, MIP-1 β, BB, RANTES, TNF- α, VEGF, IL-1 α, IL-2R α, IL-3, IL-12(p40), IL-16, IL-18, CTACK, GRO- α, HGF, ICAM-1, IFN- α 2, PDGF- α 2, PDGF, and combinations thereof, LIF, MCP-3, M-CSF, MIF, MIG, β -NGF, SCF, SCGF- β, SDF-1 α, TNF- β, TRAIL and VCAM-1.
In particular embodiments of the invention, the cytokine and/or angiogenic factor analyte comprises a human cytokine and/or angiogenic factor selected from the group consisting of IL-2, IL-4, IL-6, IL-8, IL-10, G-CSF, GM-CSF, IFN- γ, PDGF-BB, TNF- α, VEGF, HGF.
In a particular embodiment of the invention, the cytokine and/or angiogenic factor analyte comprises a human cytokine and/or angiogenic factor selected from the group consisting of IL-8, IL-10, IFN- γ, PDGF-BB, HGF.
In a particular embodiment of the invention, the cytokine and/or angiogenic factor analyte comprises a human cytokine and/or angiogenic factor selected from the group consisting of IL-8, IL-10, PDGF-BB, HGF.
In a particular embodiment of the invention, the cytokine and/or angiogenic factor analyte comprises a human cytokine and/or angiogenic factor selected from the group consisting of IL-8, PDGF-BB, HGF.
In a particular embodiment of the invention, the cytokine and/or angiogenic factor analyte comprises a human cytokine and/or angiogenic factor selected from the group consisting of IL-10 and PDGF-BB.
In a particular embodiment of the invention, the angiogenic factor analyte comprises an angiogenic factor (suitably human angiogenic factor) selected from the group consisting of follistatin, angiogenin, leptin and PECAM-1.
In a particular embodiment of the invention, the angiogenic factor analyte comprises an angiogenic factor (suitably human angiogenic factor) selected from the group consisting of follistatin, angiogenin and leptin.
In particular embodiments of the invention, the cytokine and angiogenic factor analytes are selected from the group consisting of IL-2, IL-4, IL-6, IL-8, IL-10, GM-CSF, IFN- γ, PDGF-BB, TNF- α, VEGF, HGF, follistatin, angiogenin, leptin, PECAM-1, or from the group consisting of PDGF-AA, sHER2neu, sIL-6 Ra, prolactin, sVEGFR1, IGFBP-1, IL-18, PAI-1, VEGF C, G-CSF, FGF.
In a particular embodiment of the invention, the cytokine and angiogenic factor analytes are selected from the group consisting of IL-2, IL-4, IL-6, IL-8, IL-10, G-CSF, GM-CSF, IFN- γ, PDGF-BB, TNF- α, VEGF, HGF, follistatin, angiogenin, leptin, and PECAM-1.
In particular embodiments of the invention, the cytokine and angiogenic factor analytes are selected from the group consisting of IL-8, IL-10, IFN- γ, PDGF-BB, HGF, follistatin, angiogenin, leptin, PECAM-1, or from the group consisting of PDGF-AA, sHER2neu, sIL-6 Ra, prolactin, sVEGFR1, G-CSF, FGF.
In a particular embodiment of the invention, the cytokine and angiogenic factor analytes are selected from the group consisting of IL-8, IL-10, IFN- γ, PDGF-BB, HGF, follistatin, angiogenin, leptin, and PECAM-1.
In particular embodiments of the invention, the cytokine and angiogenic factor analytes are selected from the group consisting of IL-8, IL-10, PDGF-BB, HGF, follistatin, angiogenin, leptin, PECAM-1, or from the group consisting of PDGF-AA, sHER2neu, sIL-6R α, prolactin, sVEGFR1, G-CSF, FGF.
In a particular embodiment of the invention, the cytokine and angiogenic factor analytes are selected from the group consisting of IL-8, IL-10, PDGF-BB, HGF, follistatin, angiogenin, leptin, and PECAM-1.
In particular embodiments of the invention, the cytokine and angiogenic factor analytes are selected from the group consisting of IL-8, PDGF-BB, HGF, follistatin, angiogenin, leptin, PECAM-1, or from the group consisting of PDGF-AA, sHER2neu, sIL-6 Ra, prolactin, sVEGFR1, G-CSF, FGF.
In a particular embodiment of the invention, the cytokine and angiogenic factor analytes are selected from the group consisting of IL-8, PDGF-BB, HGF, follistatin, angiogenin, leptin, and PECAM-1.
In a particular embodiment of the invention, the cytokine and angiogenic factor analytes are selected from the group consisting of IL-10, PDGF-BB, follistatin, angiogenin, and leptin.
In a particular embodiment of the invention, the cytokine and angiogenic factor analytes comprise follistatin.
Some cytokines may also be classified as angiogenic factors, and vice versa. For example, G-CSF, HGF, IL-8, PDGF-BB, VEGF, all of which are listed above under cytokines, can also be considered angiogenic factors. Thus, there is a degree of overlap, which is why the inventors believe that both cytokines and angiogenic factors are suitable for use in the methods of the invention. Thus, in some embodiments, the one or more cytokines are selected from the group consisting of IL-2, IL-4, IL-6, IL-8, IL-10, GM-CSF, IFN- γ, TNF- α; and the one or more angiogenic factors are selected from the group consisting of angiogenin, follistatin, G-CSF, HGF, IL-8, leptin, PDGF-BB, PECAM-1, VEGF.
All of the above mentioned abbreviations are summarized below. Regardless, all of the above-mentioned cytokines and angiogenic factors are well known in the art without further elaboration and are either commercially available or available in assay kits.
In preferred methods and diagnostic kits of the invention, between one and three cytokines and/or angiogenic factors are used as predetermined analytes in the method.
Analysis-diagnosis/prediction of blood samples
The methods of diagnosing and/or prognosing a brain cancer or a proliferative disorder in a subject as described herein all comprise analyzing a blood sample or a component thereof.
The method of diagnosis and/or prognosis may suitably comprise a preliminary step of obtaining a whole blood sample (i.e. having plasma and cells) from a subject. Optionally, the whole blood is then further processed to separate components of the blood (e.g., serum or plasma) and/or to remove unwanted substances (e.g., precipitates) from the blood or components thereof. Any further processing of the blood will depend on the analytical method used.
The method of diagnosis and/or prognosis suitably comprises correlating the analytical results/data relating to the determination or analysis of a blood sample (or component thereof) with favorable or unfavorable diagnostic and/or prognostic results.
Correlating the analysis results with favorable or unfavorable diagnostic and/or prognostic results may be performed manually (e.g., by a clinician or other suitable analyst) or automatically (e.g., by computer methods). The association may be established qualitatively (e.g., via comparison of graphical traces or features) or quantitatively (e.g., by reference to a predetermined threshold or statistical limit). Correlation analysis results may be performed using a predictive model, which may optionally be developed by "training" a database of pre-correlated assays and/or analyses, as defined herein.
In particular embodiments, correlating the analysis results with favorable or unfavorable diagnostic and/or prognostic results includes an initial comparison of the analysis results with a reference standard or with previous analysis results that have been correlated with favorable or unfavorable diagnostic and/or prognostic results (e.g., pre-correlated analysis results stored in a database). Correlation with previous analysis results may include statistical comparisons or "best match" comparisons (e.g., if the graphical trace is compared to those stored in a database). The method of correlating the analysis results with the favorable or unfavorable diagnostic and/or prognostic results may be a computer-implemented correlation method. Suitably, such computer-implemented methods incorporate predictive models, optionally in conjunction with a suitable database.
Suitably, the analysis results themselves are validated before any analysis results are associated. In particular, the analysis results should ideally first be verified as being deterministic and free of artifacts (artifacts) that may be caused by variations in sample preparation, etc.
In particular embodiments, the methods relate to diagnostic methods and not to prognostic methods.
In another aspect of the invention, various methods of analysis or determination of a blood sample of a subject according to the invention are used to diagnose and/or prognose a proliferative disorder in a subject. For example, a diagnostic method may comprise assaying a blood sample as defined herein and spectrally analyzing the blood sample as defined herein. One method may be stacked on top of the other. In some embodiments, the "assay" methods can be applied to any proliferative disorder (i.e., not just brain cancer) where they are used in combination with "spectroscopic" analysis methods. Thus, where these methods are used in conjunction with each other (whether in parallel or in series), any reference herein to a "determination" method of brain cancer alone can be considered to relate to any proliferative disorder or brain cancer alone.
For clarity, according to a further aspect of the invention there is provided a method of diagnosing and/or prognosing a proliferative disorder in a subject, the method comprising:
assaying a blood sample (or a component thereof) of the subject in respect of one or more (suitably pre-specified) cytokines and/or angiogenic factors; and
spectral analysis is performed on a blood sample (or component thereof) of a subject to generate spectral features characteristic of the blood sample (or component thereof).
Determination of blood samples
Determining the blood sample suitably comprises determining the level of one or more cytokines and/or angiogenic factors in the blood sample. In a particular embodiment, the blood sample assayed is a serum sample. In another embodiment, the blood sample assayed is a plasma sample.
Optionally, the levels (or concentrations) of one or more cytokines and/or angiogenic factors may be calibrated or normalized with respect to standard markers that are either intrinsic to the blood (i.e., molecules that are typically present at substantially constant levels in the blood of all subjects) or added to the blood to provide a known concentration of the substance in the blood, thereby eliminating the diluting effect on the analytical data.
The levels (or calibrated/normalized levels) of the one or more cytokines and/or angiogenic factors can be evaluated, for example, against a predetermined threshold for each of the one or more cytokines and/or angiogenic factors (e.g., as determined by prior studies of cytokine/angiogenic factor levels in blood samples of representative cross-sectional slices of subjects with and without brain cancer or a proliferative disorder) or relative to one another (e.g., comparing relative levels/profiles of the cytokines/angiogenic factors of interest). Such an assessment can then be correlated with a diagnosis and/or prognosis. In particular, observations of elevated or reduced levels of each of one or more cytokines and/or angiogenic factors, whether relative to a predetermined threshold or relative to each other, may be correlated with favorable or unfavorable diagnostic and/or prognostic outcomes. For example, in particular embodiments, correlation with favorable or unfavorable diagnostic and/or prognostic outcomes may be made with reference to one or more ratios between sets of particular cytokines and/or angiogenic factors. In particular embodiments, the ratio of PECAM-1 to PDGF-BB levels may be used to determine a favorable or unfavorable diagnostic and/or prognostic outcome.
In particular embodiments, the blood sample is assayed using an immunoassay, e.g., based on an antigen-antibody reaction.
Assaying the blood sample may comprise any suitable assay known in the art. Each of the one or more cytokines and/or angiogenic factors may be determined separately, optionally in series. In this way, a blood sample can be divided into multiple aliquots for testing. Alternatively, each of the one or more cytokines and/or angiogenic factors may be assayed in parallel fashion (e.g., as multiple aliquots). Alternatively, each of the one or more cytokines and/or angiogenic factors may be assayed in parallel in the same assay (i.e., with a single blood sample), e.g., by a multiplex assay.
In particular embodiments, the blood sample is assayed using a magnetic bead-based multiplex assay designed to measure multiple cytokines and/or angiogenic factors. The multiplexing feature makes it possible to quantify the levels of multiple proteins in a single well within only 3 hours, using as little as 12.5 μ l of serum or plasma. Suitable assay kits include Bio-PlexTMAnd Bio-PlexTMPro system incorporating magnetic beads into its design. The magnetic beads allow the option of using magnetic separation during the washing step rather than vacuum filtration. Magnetic separation allows for greater automation without significant changes to the standard Bio-Plex assayAnd (5) determining a scheme. Standard Bio-Plex assay protocolwww.bio-rad.com/bio-plex/Available on-line and described in Bio-Plex ProTMMeasurement manual-http://www.bio-rad.com/webroot/web/pdf/lsr/literature/ 10014905.pdf。
The assay suitably uses multiple fluorescently stained beads (e.g., xMAP technology) to simultaneously detect multiple cytokines and/or angiogenic factors in a single assay (e.g., a single well). Thus, two or more cytokines and/or angiogenic factors may be the subject of the analysis. In particular embodiments, up to 100 unique fluorescently stained beads are used for cytokine/angiogenic factor detection.
The assay suitably uses a flow cytometer with two lasers and associated optics to measure the different cytokines/angiogenic factors bound to the surface of the beads.
The assay suitably uses a diagnostic kit with a (high speed) digital signal processor that efficiently controls fluorescence data.
The bead-based assay suitably operates in a similar manner to a capture sandwich immunoassay (capture sandwich immunoassay). For example, antibodies directed against the desired cytokine and/or angiogenic factor targets are suitably covalently bound to the internally stained beads. During the assay, the beads are suitably contacted with the relevant blood sample to facilitate a reaction between the covalently bound antibody and the target cytokine and/or angiogenic factor. After a sufficient contact time, the beads are suitably washed (optionally several times) to remove unbound protein. Thereafter, a biotinylated detection antibody specific for an epitope different from that of the capture antibody is suitably added to the bead reaction mixture. This suitably creates a sandwich of antibodies surrounding the cytokine/angiogenic factor target. A reporter complex, such as streptavidin-phycoerythrin (streptavidin-PE), is then suitably added to bind the biotinylated detection antibody on the bead surface.
Data is suitably acquired from the bead reaction mixture using a suitable reader system. In a particular embodiment, a Bio-Plex system (or Luminex system), a dual wavelength laser is usedThe reader, the fluid-based microplate reader system, obtains the data. The bead reaction mixture is suitably aspirated into the reader system. The laser and associated optics suitably detect the internal fluorescence of the individual stained beads as well as the fluorescent reporter signal on the bead surface. This suitably identifies each assay and reports the level of cytokine/angiogenic factor in the sample. The intensity of fluorescence detected on the beads is indicative of the relative amount of the target cytokine and/or angiogenic factor molecule in the sample being tested. The digital processor suitably controls the data output, possibly using a Bio-Plex ManagerTMThe software is suitably further analyzed and presented as Fluorescence Intensity (FI) and target concentration data.
The levels of one or more cytokines and/or angiogenic factors may then be used as described above to determine a favorable or unfavorable diagnostic and/or prognostic outcome either manually or automatically (i.e., by direct computer processing of the data as defined herein).
In some embodiments, determining the subject's blood sample (or component thereof) in terms of one or more (suitably pre-specified) cytokines and/or angiogenic factors is performing a spectroscopic analysis on the subject's blood sample (or component thereof) to produce a spectroscopic signature characteristic of the blood sample (or component thereof). It is envisioned that changes in the blood with respect to one or more (suitably pre-specified) cytokines and/or angiogenic factors may result in changes in the relevant spectral characteristics.
Spectroscopic analysis of blood samples
Most suitably, the blood sample used in the spectroscopic analysis is serum or plasma, most preferably serum. The blood sample is preferably human serum.
The spectroscopic analysis may comprise any such analysis known in the art. For example, spectroscopic analysis may include Infrared (IR), Ultraviolet (UV), Nuclear Magnetic Resonance (NMR), raman, and many other possible forms of spectroscopy.
However, in a preferred embodiment, the spectroscopic analysis is Infrared (IR) spectroscopic analysis. Suitably, the IR spectroscopy is fourier transform IR (ftir) spectroscopy, suitably using at least 10 scans, suitably at least 15 scans, suitably at least 30 scans. Suitably, the FTIR spectroscopy uses up to 100 scans, suitably up to 50 scans and most suitably up to 40 scans. In a preferred embodiment, 32 scans are used. Suitably, the scans are additive. The number of scans is suitably chosen to optimize data capacity and data acquisition time.
Suitably at a wave number (cm) of 400-4000-1) The IR spectrum is collected. Suitably the IR spectrum has 10cm-1Or smaller, suitably 5cm-1Or less, especially 4cm-1The resolution of (2). The spectral characteristic (i.e. the characteristic region) specific to the blood sample is suitably in the range 500 to 2500cm-1In between, more suitably at 800 and 2000cm-1In between, and most suitably between 900 and 1800cm-1Spectrum (total) in between.
Suitably, the spectral analysis comprises vector normalization as a pre-processing step.
In a preferred embodiment, the FTIR spectroscopic analysis is attenuated Total reflectance FIR (ATR-FTIR). The high information content of the corresponding features due to the way in which the evanescent waves inherent to such spectroscopic techniques interact with the blood sample is a particularly efficient form of spectroscopy for diagnosing and/or predicting proliferative disorders from blood samples. ATR is a specific sampling technique that enables direct detection of samples in either a solid or liquid state. An "ATR crystal" is suitably used to support a blood sample during IR analysis. Suitably, the blood sample covers the surface of the "ATR crystal" (or a portion thereof) during IR analysis. Suitable ATR crystals include germanium, KRS-5 zinc selenide, diamond and silicon. The ATR crystal is suitably in plate-like form. In a particular embodiment, the ATR crystal is a single reflective diamond crystal.
During ATR-FTIR analysis, a blood sample is loaded onto the ATR crystal, and IR light suitably passes through the ATR crystal and reflects (suitably via total internal reflection) at least once from an internal surface in contact with the sample. Such reflections form "evanescent waves" that penetrate the blood sample to an extent that depends on the wavelength of the light, the angle of incidence, and the refractive indices of the ATR crystal and the blood sample itself. The number of reflections can be varied by varying the angle of incidence. Suitably, the beam is ultimately received by the IR detector because of its presence of the crystal.
The "evanescent wave" only works if the ATR crystal is an optical material with a higher refractive index than the blood sample. Thus, in the context of the present invention, ATR-FTIR can be optimized by careful sample preparation.
A (relatively thin) film of blood sample is suitably applied to the ATR crystal surface prior to FTIR analysis. The sample is suitably prepared so as to contain minimal or no entrapped air. The blood sample membrane (or at least the portion thereof exposed to the IR analysis) is suitably of (substantially) uniform thickness, suitably within a tolerance of +/-40 μm or less, more suitably within a tolerance of +/-20 μm or less, most suitably within a tolerance of +/-10 μm or less. The average film thickness of the blood sample across the surface of the ATR crystal (or at least the portion thereof exposed to IR analysis) is suitably between 0.1 and 200 μm, suitably between 1 and 100 μm, suitably between 2 and 50 μm. The maximum film thickness (i.e. the point of maximum thickness) of the blood sample across the surface of the ATR crystal (or at least the portion thereof exposed to IR analysis) is suitably between 1 and 200 μm, suitably between 2 and 100 μm, suitably between 5 and 50 μm, or suitably between 2 and 8 μm. The minimum film thickness (i.e. the point of minimum thickness) of the blood sample across the surface of the ATR crystal (or at least the portion thereof exposed to IR analysis) is suitably between 0 and 40 μm, suitably between 1 and 20 μm, suitably between 2 and 10 μm.
A blood sample film of suitable thickness is suitably obtained by depositing 0.1-10 μ L, suitably 0.2-5 μ L, most suitably 0.5-1.5 μ L (or about 1 μ L) of said blood sample on the surface of the ATR crystal. Suitably, the deposited blood sample is then allowed to dry to produce a blood sample film of suitable thickness. Suitably, drying is effected at Standard Ambient Temperature and Pressure (SATP) (i.e. about 25 ℃, at 100kPa) for between 2 and 32 minutes, more suitably between 4 and 16 minutes, most suitably about 8 minutes, or under other equivalent conditions which result in the same level of drying. Analysis of the obtained film by white light interferometry may indicate the thickness of the film across the surface of the ATR crystal, thereby verifying the appropriate film thickness. The present inventors have found that creating a film of suitable thickness can reduce the characteristic differences associated with sample preparation, so that any observed differences in characteristics from blood sample to blood sample can be more reliably attributed to different compositions rather than variability in sample preparation.
Suitably, a single aliquot taken from a large number of blood samples is used for each spectral analysis. In this way, additional aliquots may then be used for additional spectral analysis of the sample blood sample, thereby facilitating validation of the results. Suitably, at least two spectral analyses are performed on each blood sample. Furthermore, suitably, each individual spectral analysis is repeated at least twice, preferably at least three times, with the same aliquot to aid in validating the results.
The characteristics of the blood sample can then be characterized (in the characterization region, typically at 900--1) Correlated with a favorable or unfavorable diagnostic and/or prognostic outcome, or otherwise used to detect cancer cells in a subject. Such correlation is possible by comparing the features with one or more previously correlated features (i.e., features previously obtained and confirmed by, for example, biopsy as indicative of a favorable or unfavorable diagnostic and/or prognostic outcome). This may be achieved by way of qualitative assessment-for example, certain features will be similar (possibly to a different extent) to those characteristic of a blood sample of a subject with a proliferative disorder, while other features may be different from such features. Thus, qualitative assessments of the characteristic manifestations can be used to diagnose and/or prognose proliferative disorders. Such qualitative evaluations may be performed manually, but are preferably performed digitally by a computer running on computer software performing such evaluations. Suitably, any such computer software is capable of correlating the features with favorable or unfavorable diagnostic and/or prognostic outcomes based on the evaluations.
Alternatively or additionally, the correlation with a favorable or unfavorable diagnostic and/or predictive result may be performed by quantitative evaluation-for example, where the blood sample characteristics are compared with one or more reference characteristics (which have been previously correlated with a favorable or unfavorable diagnostic and/or predictive result), optionally stored in a database, and a statistical analysis is suitably performed for the likelihood of correlation.
In particular embodiments, the spectrally obtained features are compared to a plurality of pre-correlated features stored in a database (e.g., a "training set") in order to derive a correlation to a favorable or unfavorable diagnostic and/or prognostic outcome. Statistical analysis is preferably suitably performed (e.g., by pattern recognition algorithms) based on a comparison of the similarity and dissimilarity of the features to the pre-associated features, which are then used to associate the features with favorable or unfavorable diagnostic and/or prognostic outcomes. Suitably, the pattern recognition algorithm comprises a Support Vector Machine (SVM) and principal component discriminant function analysis (PC-DFA).
In particular embodiments, the spectrally obtained features are correlated with favorable or unfavorable diagnostic and/or predictive results based on predictive models developed through a database of "trained" (e.g., through pattern recognition algorithms) pre-correlated analyses.
The detection and/or comparison of blood sample characteristics from spectroscopic analysis does not necessarily concern a particular peak or a particular substance responsible for any particular peak. However, in the case of ATR-FTIR, it is typically as at about 1550cm-1And 1650cm-1The two amide peaks that appear in the doublet of (especially when TSPA is used as an internal standard) appear to be important indicators of proliferative disorders, as certain changes in these peaks indicate changes in the protein structure that represents a proliferative disorder.
In a particular embodiment, the blood sample is whole serum.
In a particular embodiment, the blood sample is whole serum from which components above 100kDa have been removed by centrifugal filtration.
In a particular embodiment, the blood sample is whole serum from which components above 10kDa have been removed by centrifugal filtration.
In a particular embodiment, the blood sample is whole serum from which components above 3kDa have been removed by centrifugal filtration.
Any such centrifugation can be performed using a microcentrifuge in combination with a suitable protein filter at 14,000rpm according to the manufacturer's instructions (Amicon membrane filter, Merck Millipore).
In some embodiments of the invention, multiple spectral analyses are performed using multiple sera derived from the same whole blood sample (i.e., with different degrees of filtration), and the results are compared and/or used for cross-validation.
Database, computer software and computer implemented method
The present invention provides a database comprising a plurality of data sets, each set relating to the amount of one or more cytokines and/or angiogenic factors in a particular blood sample (or component thereof) of a particular subject, each set being associated with a favorable or unfavorable diagnostic and/or prognostic outcome relating to brain cancer in the particular subject.
The present invention provides a database comprising a plurality of spectral features, each feature being associated with a particular blood sample (or component thereof) of a particular subject, each feature being associated with a favorable or unfavorable diagnostic and/or prognostic outcome relating to a proliferative disorder in said particular subject.
The present invention provides a computer readable medium (e.g., an optical disc) containing a database as defined herein.
The databases of the present invention are suitably established by assaying or spectroscopically analyzing a plurality of blood samples from different subjects to generate analytical data for each blood sample, and then systematically correlating the analytical data with favorable or unfavorable diagnostic and/or prognostic outcomes related to a proliferative disorder in the corresponding subject. Correlation of the analytical data with favorable or unfavorable diagnostic and/or prognostic outcomes is suitably accomplished by methods well known in the art, including biopsy. The analytical data may further be correlated with the degree of benefit or disadvantage of the diagnostic result and/or the predictive result (i.e., the severity, aggressiveness, and/or degree of the detected proliferative disorder).
In the case of a database comprising a plurality of spectral features, the database may be established by first obtaining a plurality of blood sample features from a representative sample of a subject identified as having a proliferative disorder (or as having a proliferative disorder of some severity, aggressiveness, and/or degree), and a plurality of blood sample features from a representative sample of a subject identified as not having a proliferative disorder. Suitably, the sample may also be matched to other criteria, such as sex or age, to help normalize differences between subjects otherwise associated with the same proliferative disorder state.
The predictive model may further be built from a database by "training" the data. Such models may then be incorporated into computer software for purposes of later prediction. The features may then be all combined and (optionally, randomly or selectively) divided into a "training set" of features (preferably more than 50%, suitably about 66% of the features are selected for the training set) and a "blind set" of features. The "training set" is then suitably trained using pattern recognition algorithms (e.g., using support vector machines such as those available through libsv or PC-DFA) suitably by performing a grid search to optimize the cost and gamma functions to ensure that it can identify the training set, thereby producing a viable predictive model. The "blind set" may then be provided to a model, which is then requested to predict whether individual features in the blind set should be associated with favorable or unfavorable diagnostic and/or predictive outcomes. The predictions may then be translated into a "confusion matrix" that sets forth which predictions were made. These predictions can then be validated (e.g., by confirming actual results, e.g., from a biopsy) to calculate the sensitivity and specificity of the model.
The predictive model suitably has a sensitivity of greater than 75%, more suitably greater than 80%, most suitably greater than 85%. The predictive model suitably has a specificity of greater than 85%, more suitably greater than 90%, most suitably greater than 98%.
Naturally, the model may be updated and improved as further results are obtained and associated and further criteria and variables are considered.
After building, the model may be incorporated into diagnostic computer software. A computer running under the diagnostic computer software (and optionally also a database) is then suitably configured by the software to perform predictive diagnosis and/or prediction (suitably with sensitivity and specificity as established above) on newly entered unassociated features to correlate the features with favorable or unfavorable diagnostic and/or predictive outcomes.
As such, the present invention provides a computer having installed thereon diagnostic computer software configured to operate the computer to perform predictive diagnosis and/or prognosis relating to a proliferative disorder based on the spectral characteristics of a blood sample of a subject. Suitably, the diagnostic computer software incorporates a predictive model derived from one or more pattern recognition algorithms applied to a plurality of pre-associated features. The computer may also be equipped with a database as defined herein to facilitate correlation of results.
In another aspect of the invention, a computer readable medium is provided, containing diagnostic computer software as defined herein.
In another aspect of the invention there is provided a computer-implemented method of correlating the results of an assay or spectroscopic analysis as defined herein with a favorable or unfavorable diagnostic and/or prognostic outcome, the method comprising:
-collecting data from said determination or spectroscopic analysis;
using a predictive model, suitably based on a pattern recognition algorithm implemented on a pre-correlated assay or spectral analysis (optionally in conjunction with a database as defined herein), to correlate said data with a favorable or unfavorable diagnostic and/or predictive result.
Diagnostic kit
The present invention provides a diagnostic kit for diagnosing and/or prognosing brain cancer in a subject, the kit comprising a device configured to receive a blood sample (or a component thereof) from the subject and to assay the blood sample (or a component thereof) for one or more (suitably pre-specified) cytokines and/or angiogenic factors; and means (optionally the same as mentioned above) for correlating or contributing to the correlation of the amount of the one or more cytokines and/or angiogenic factors in the blood sample (or component thereof) with a favorable or unfavorable diagnostic and/or prognostic outcome.
The present invention provides a diagnostic kit for diagnosing and/or prognosing a proliferative disorder in a subject, the kit comprising a device configured to receive a blood sample (or component thereof) from the subject and perform a spectroscopic analysis on the subject's blood sample (or component thereof) to generate a spectroscopic signature characteristic of the blood sample (or component thereof); and means (optionally the same as mentioned above) for correlating or contributing to the correlation of said spectral feature of said blood sample (or component thereof) with a favorable or unfavorable diagnostic and/or prognostic outcome.
In some embodiments, the device for assaying or analyzing a blood sample is the same device used to correlate or facilitate the correlation of results. The diagnostic kit of the present invention may be a single unitary device for receiving and assaying/analyzing a sample and also correlating the results of said assaying/analyzing with favorable or unfavorable diagnostic and/or prognostic results.
In a preferred embodiment, the means for correlating or facilitating the correlation of results is operable to perform a computer-implemented correlation method as defined herein. Suitably, the means for correlating or facilitating correlation of results comprises or is in communication with a computer (e.g. whether wired or wireless) configured with software to correlate the results with beneficial or disadvantageous diagnostic and/or prognostic results.
In the case of a diagnostic kit for spectroscopic analysis of a blood sample, the associating means suitably comprises or is in communication with a computer as defined herein, said computer being equipped with diagnostic computer software configured to operate the computer to perform a predictive diagnosis and/or prognosis relating to a proliferative disorder based on a spectroscopic signature of the blood sample of the subject.
In the case of a diagnostic kit for spectroscopic analysis of a blood sample, a device configured to receive a blood sample may be configured to automatically prepare a blood sample (or components thereof) as defined herein. For example, in the case of ATR-FTIR, the device may be configured to automatically produce a film of blood sample of the required thickness on the ATR crystal before the IR analysis begins. The apparatus may include a film thickness verification device (e.g. a white light interferometer) to verify the correct thickness of the blood sample on the ATR crystal.
The diagnostic kit may be configured to automatically perform any of the method steps defined herein, optionally by a computer-implemented method.
Examples
Example 1 determination of blood samples
In this example, Pro by Bio-Plex was usedTMThe magnetic bead-based multiplex assay provided by the assay kit performs cytokine and angiogenic factor assays on plasma samples. All relevant protocols fully followed in this example are listed under the heading "Bio-Plex ProTMInstruction manual for determination of cytokines, chemokines and growth factors (Bio-Plex ProTMThe Instruction Manual of Assays cytokines, Chemokine, and Growth Factors) at the websitewww.bio-rad.comAnd in particular inhttp://www.bio-rad.com/webroot/web/pdf/ lsr/literature/10014905.pdfAvailable from Bio-Rad Laboratories, Inc. The protocol of this instruction manual followed the protocol for "Bio-Plex ProTMHuman, mouse and rat cytokine assays (Bio-Plex ProTMHuman,Mouse,and Rat Cytokine Assays)”。Bio-PlexTMThe system follows the system described in the instruction manualReady, properly calibrated and verified as described. Magnetic beads present in a 96-well Bio-Plex Pro flat bottom plate were washed by magnetic separation using a magnetic device of the Bio-Plex Pro washing station (wash station). The 96 well Bio-Plex Pro flat bottom plate was appropriately laid out with the wells appropriately allocated. Suitable standards provided by the Bio-plex system are prepared according to the protocols listed in the instruction manual.
Bio-Plex as described in the instruction manualTMThe floating array system is built around three core elements of xMAP technology:
fluorescently stained microspheres (also called beads), each with a unique color code or spectrum
Addresses to allow discrimination of individual tests in multiple suspensions. This allows singulation of microplates in 96 wells
Simultaneous detection of more than 100 different types of molecules in a well
Special flow cytometry with two lasers and associated optics to measure binding to beads
Of the surface of (a)
High speed digital signal processor for efficient control of fluorescence data
Bio-Plex ProTMCytokine, chemokine and growth factor assays are essentially immunoassays designed on magnetic beads. The assay principle is similar to that of sandwich ELISA (figure 1). Capture antibodies to the desired biomarkers are covalently coupled to the beads. The conjugated beads are reacted with a sample containing the biomarker of interest. After a series of washes to remove unbound protein, biotinylated detection antibody is added to create a sandwich complex. The final detection complex is formed by adding a streptavidin-phycoerythrin (SA-PE) conjugate. Phycoerythrin is used as a fluorescent indicator or reporter.
As also explained in the instruction manual, data from the reaction were obtained using the Bio-Plex system or similar Luminex-based reader. For example, when a multiplex assay suspension is aspirated into a Bio-Plex 200 reader, a red (635nm) laser illuminates the fluorescent dye in each bead to provide bead classification and thus assay identification. At the same time, greenThe (532nm) laser excited the PE to produce a reporter signal that was detected by a photomultiplier tube (PMT). High speed digital processor controls data output and Bio-Plex ManagerTMThe software presents the data as Mean Fluorescence Intensity (MFI) and concentration (pg/mL). The concentration of analyte bound to each bead is proportional to the Mean Fluorescence Intensity (MFI) of the reporter signal.
The instruction manual summarizes the initial preparation of the assay as follows:
1. layout of planning board
2. Start/Heat Bio-Plex System (up to 30min)
At the same time, the assay reagents are equilibrated to Room Temperature (RT)
Start thawing of the sample
3. Ready to wash station or calibrating vacuum manifold
4. Calibration System (now or later during incubation)
5. Reconstitute single vial of standard in 500. mu.l of appropriate diluent, vortex and incubate on ice (30min)
For serum and plasma samples (according to this example), use of Bio-Plex standard diluent
6. An 8-point standard dilution series and blank were prepared.
Add 72 μ l of diluent to tube S1 and 150 μ l of diluent to tube S2-8 and the blank.
Transfer 128. mu.l of reconstituted standard into S1
Serial dilutions from S1 to S8 were then made 4-fold by transferring 50 μ Ι between tubes. Swirling between transfers
7. After thawing, 1 × sample was prepared
Dilution of serum, plasma and lysate with Bio-Plex sample diluent
8. Preparation of 1 × coupled beads in assay buffer protected from light
From 10 × stock solution: mu.l of beads were added to 5,175. mu.l of buffer
From 20 × stock solution: 288. mu.l of beads were added to 5,472. mu.l of buffer
9. Ensure samples and standards are at room temperature prior to dispensing
The instruction manual summarizes the operation of the assay as follows:
1. filter plates were pre-wetted with 100. mu.l assay buffer (skip flat bottom)
2. 50 μ l of 1 × beads were added to the assay plate
3. Wash 2 times with 100. mu.l of wash buffer
4. Add 50. mu.l of sample, standard, blank, control
5. Capped and incubated at room temperature in the dark while shaking at 300RPM
30 min-human group I, II and mouse group I, II
With the remaining 10min, 1 × detection Ab in detection antibody diluent was prepared
From 10 × stock solution: add 300. mu.l Ab to 2,700. mu.l diluent
From 20 × stock solution: add 150. mu.l Ab to 2,850. mu.l diluent
6. Wash 3 times with 100. mu.l of wash buffer
7. Add 25. mu.l of detection antibody
8. Capped and incubated at room temperature in the dark while shaking at 300RPM
30 min-human group I, II; mouse group I, II
Meanwhile, preparing a software scheme; input normalized Standard S1 value
For the remaining 10min, 1 × SA-PE in assay buffer was prepared from 100 × stock: mu.l SA-PE was added to 5,940. mu.l assay buffer. Light-shielding
9. Wash 3 times with 100. mu.l of wash buffer
10. Add 50. mu.l of streptavidin-PE
11. Capped and incubated at room temperature in the dark while shaking at 300RPM
10 min-human group I, II; mouse group I, II
12. Wash 3 times with 100. mu.l of wash buffer
13. The beads were resuspended in 125. mu.l assay buffer and shaken at 1100RPM for 30sec
14. Reading board
Low PMT (low RP1) -human group I, II; mouse group I, II
Bio-Plex Pro for human, mouse and rat cytokine assays according to the instruction manualTMReagents provided by the assay kit include (table 1):
TMTABLE 1-test provided by Bio-Plex Pro assay kit for human, mouse and rat cytokine assay
Preparation:
*Bio-Piex ProTMthe high dilution kit includes 70ml of serum-based diluent instead of standard diluent and sample diluent
According to the instruction manual, cytokines that can be tested include (table 2):
TABLE 2 checkable cytokines
However, additional cytokines and angiogenic factors were actually tested and relevant standards and protocols were developed accordingly. These additional cytokines and angiogenic factors are described in detail in the results section.
Whole blood sampling
Whole blood samples were collected from 50 glioma patients and 27 healthy subjects.
Preparation of plasma samples from Whole blood samples
Plasma samples of each of 50 glioma patients and 27 healthy subjects were prepared by: the corresponding fresh whole blood sample was added to the tube containing the anticoagulant and spun at 13,200rpm for 10min at 4 ℃ until the blood cells fell to the bottom of the tube to clear the precipitated sample. The plasma is then decanted or aspirated. The obtained plasma has a density of about 1025kg/m3Or a density of 1.025 kg/l. The plasma samples were then measured immediately or otherwise aliquoted and stored at-70 ℃ in single use aliquots for later use, however avoiding repeated freeze/thaw cycles.
Prior to performing the assay, 1 volume of the plasma sample is diluted with 3 volumes of sample diluent (e.g., 50 μ L sample +150 μ L sample diluent).
Preparation of coupled beads
Now use Bio-PlexTMThe protocol adopted in the Pro instruction manual describes the preparation of coupled beads.
Each kit included 1 tube of coupled beads. Instructions for diluting the coupled beads to 1 x concentration are provided.
When 10 packaged reagents are used, it is ensured that only the required volumes of coupled beads, detection antibody, streptavidin-PE and buffer are removed from the tube or vial. For example, transfer into a 50ml reservoir of assay buffer sufficient to perform all steps of the assay procedure (i.e., pre-wet filter plate, dilute coupled beads, dilute streptavidin-PE, and re-suspend beads).
1. The volume of beads and assay buffer required for coupling was calculated using the calculation worksheet shown below.
2. The desired volume of assay buffer was added to a 15ml polypropylene tube.
3. The coupled beads were vortexed at moderate speed for 30 sec. The lid is carefully opened and any liquid that falls into the lid is pipetted back into the tube. This is important to ensure maximum bead recovery. The vials were not centrifuged; doing so causes the beads to clump.
4. Pipette the desired volume of the stock solution of coupled beads into a 15ml tube containing assay buffer to dilute the coupled beads to 1 × concentration. Each well required 5 μ l of coupled beads (10 ×) or 2.5 μ l of coupled beads (20 ×) adjusted to a final volume of 50 μ l using assay buffer. Calculated with reference to the exemplary beads in tables 3-6 below, which include a 20% excess to compensate for transfer loss.
Table 3-1 x coupled beads were prepared from a 10 x stock solution. Pre-mixed plates or a single weight assay
| Number of holes | 10X bead (ul) | Assay buffer (μ l) | Total volume (μ l) |
| 96 | 575 | 5,175 | 5,750 |
| 48 | 288 | 2,587 | 2,875 |
Table 4-1 x coupled beads were prepared from a 10 x stock solution. Mixed single weight assay
Table 5-1 x coupled beads were prepared from 20 x stock solution. Pre-mixed plates or a single weight assay.
| Number of holes | 20X bead (ul) | Assay buffer (μ l) | Total volume (μ l) |
| 96 | 288 | 5,472 | 5,760 |
| 48 | 144 | 2,736 | 2,880 |
Table 6-1 x coupled beads were prepared from 20 x stock solution. Mixed single weight assay
5. Aluminum foil was used to protect the beads from light. Equilibrating at room temperature for 20min before use
Magnetic bead based multiplex assay
Then as Bio-PlexTMPro instruction manual (also listed below) to run the assay.
All buffers, diluted standards, diluted conjugated beads and samples were returned to room temperature prior to use. To ensure optimal performance, carefully aspirate with a calibrated pipette (avoid foaming) and use a new pipette tip.
The coupled beads, standards and sample are added and then:
1. the unused holes are covered with a sealing tape.
2. Pre-wetting the filter plate.
3. The diluted coupled beads were vortexed at moderate speed for 30 sec.
The diluted coupled beads were poured into a reagent reservoir and 50 μ Ι was added per well.
And (4) prompting: multichannel pipettes are highly recommended due to ease of use and efficiency.
4. The wells were washed twice with the selected washing method.
5. Dilute standards, blanks, samples and controls (if applicable) with gentle vortex for 1-3 sec. 50 μ l of diluted standards, controls or samples were added to each well and the pipette tips were changed after each volume transfer.
6. Incubate on a shaker at room temperature, as detailed in table 7 below.
TABLE 7 incubation time determined
| Measurement of | Incubation time |
| Bio-Plex Pro human cytokine (groups I and II) | 30min |
| Bio-Plex Pro mouse cytokines (groups I and II) | 30min |
| Bio-Plex Pro mouse cytokine (group III) | 1hr |
| Bio-Plex Pro rat cytokine (group I) | 1hr |
| Bio-Plex Pro TGF-β | 2hr |
And annotating: the incubation time has been optimized for each assay and should not exceed 4 hr. Consistent with this incubation time for optimum reproducibility.
Preparation and addition of detection antibody.
Each kit contained 1 tube of detection antibody. Instructions for diluting the detection antibody to 1 x concentration are provided.
1. Upon incubation of the samples, the volumes of detection antibody and desired detection antibody diluent were calculated using the calculation worksheet shown below. The detection antibody should be prepared 10-15min before use.
2. The desired volume of detection antibody diluent was added to the 15ml tube.
3. Vortex detection antibody at moderate speed for 15-20sec, then perform 30sec rotation to collect the entire volume at the bottom of the vial.
4. The required volume was pipetted from each detection antibody tube into a 15ml polypropylene tube. Each well of the assay needs to be adjusted to a final volume of 25. mu.l of either 2.5. mu.l of detection antibody (10X) or 1.25. mu.l of detection antibody (20X).
Reference is made to the exemplary detection antibody calculations in tables 8-11 below. These calculations included a 25% excess to compensate for transfer losses.
Tables 8-11 summarize the volume required to prepare 1 × detection antibody from either 10 × or 20 × stock solutions alone. The volume of 1 antibody prepared when mixing two 10 x or two 20 x stock solutions is also shown. For instructions to prepare 1 × antibodies from two stock solutions of different concentrations (e.g., when mixing human diabetes (20 ×) with the human group I assay (10 ×)), see Bio-PlexPro diabetes instruction manual (publication No. 10010747).
Table 8-1 x coupled beads were prepared from a 10 x stock solution. Pre-mixed plates or a single weight assay
| Number of holes | 10X detection antibody (. mu.l) | Detection ofAntibody Diluent (μ l) | Total volume (μ l) |
| 96 | 300 | 2,700 | 3,000 |
| 48 | 150 | 1,350 | 1,500 |
Table 9-1 x coupled beads were prepared from a 10 x stock solution. Mixed single weight assay
Table 10-1 x coupled beads were prepared from 20 x stock solution. Pre-mixed plates or a single weight assay.
| Number of holes | 20X detection antibody (. mu.l) | Detection antibody Diluent (μ l) | Total volume (μ l) |
| 96 | 150 | 2,850 | 3,000 |
| 48 | 75 | 1,425 | 1,500 |
Table 11-1 x coupled beads were prepared from 20 x stock solution. Mixed single weight assay
5. After incubation of the samples, the sealing tape was slowly removed and discarded.
6. Washed three times with the selected washing method.
7. The diluted detection antibody was vortexed gently for 1-3 sec. The diluted detection antibody was poured into the reagent reservoir and 25 μ Ι was added to each well using a multichannel pipette.
8. The plate was covered with a new sealing tape and the hole was sealed. Incubate on a shaker at room temperature, as detailed in table 12 below.
TABLE 12 determination of incubation time
| Measurement of | Incubation time |
| Bio-Plex Pro human cytokine (groups I and II) | 30min |
| Bio-Plex Pro mouse cytokines (groups I, II, III) | 30min |
| Bio-Plex Pro rat cytokine (group I) | 30min |
| Bio-Plex Pro TGF-β | 1hr |
Preparation and addition of streptavidin-PE
1. In incubating the detection antibody, the volumes of streptavidin-PE (100 x) and the required assay buffer were calculated using the calculation worksheet shown below. Each well required 0.5. mu.l streptavidin-PE (100X) adjusted to a final volume of 50. mu.l with assay buffer. streptavidin-PE (100X) should be prepared 10min before use.
2. The desired volume of assay buffer was added to the 15ml tube.
3. Vortex the streptavidin-PE tube at medium speed for 15-20 sec. A 30sec rotation was performed to collect the entire volume at the bottom of the vial.
4. The required volume of streptavidin-PE was pipetted into a 15ml polypropylene tube containing assay buffer to dilute the streptavidin-PE to 1 x concentration.
Table 13 below shows an exemplary calculation of dilution streptavidin-PE, which includes a 25% excess to compensate for transfer loss. streptavidin-PE was protected from light until ready for use.
TABLE 13 preparation of streptavidin-PE from 100 Xstock solution
| Number of |
100 × streptavidin-PE (μ l) | Assay buffer (μ l) | Total volume (μ l) |
| 96 | 60 | 5,940 | 6,000 |
| 48 | 30 | 2,970 | 3,000 |
5. After the detection antibody incubation, the sealing tape is slowly removed and discarded.
6. Washed three times with the selected washing method.
7. Vortex diluted streptavidin-PE for 3-5sec at moderate speed. The diluted streptavidin-PE was poured into the reagent reservoir and 50 μ Ι was added to each well using a multichannel pipette.
8. Incubations were carried out on a shaker at room temperature for the times specified in Table 14 below.
TABLE 14-determination of incubation time
| Measurement of | Incubation time |
| Bio-Plex Pro human cytokine (groups I and II) | 10min |
| Bio-Plex Pro mouse cytokines (groups I, II, III) | 10min |
| Bio-Plex Pro rat cytokine (group I) | 10min |
| Bio-Plex Pro TGF-β | 30min |
9. After the streptavidin-PE incubation step, the sealing band was slowly removed and discarded.
10. The wells were washed three times with the selected washing method.
11. Add 125 μ Ι assay buffer to each well. The plate was covered with a new sealing tape. The plate was shaken at 1,100rpm at room temperature for 30sec and the sealing tape was slowly removed. Ensuring that the plate cover has been removed before placing the plate on the reader.
Reading assay plate
The assay plate was read according to the instruction manual as described below.
Recommendation Bio-PlexManagerTMThe software was used for all Bio-Plex Pro assay data acquisition and analysis. Also included is a description for Luminex xPONENT software. For instructions using other xMAP system software packages, experts were applied in connection with Bio-Rad technical support or the Bio-Rad field of your region.
Protocols should be prepared in advance so that the plate is read as soon as the experiment is complete. Protocol files specify the analytes used in the reading process, the plate wells being read, sample information, values for standards and controls, and instrument settings.
Protocols can be obtained from Bio-Plex management software version 6.0 or created from a file menu. Version 6.0 of the Bio-Plex management software contains protocols for most of the Bio-Plex assays. The solution should select the new solution that should be created.
The protocol was prepared by the following steps:
1. the protocol is described and information about the assay is entered.
2. Analytes (from table 2 above) were selected.
3. The format of the plate is designed according to the plate layout template created for the assay.
4. Details of the standard are entered-e.g. the highest concentration of each analyte, dilution factor, batch number, etc.
5. Control information is entered, including concentration and dilution information for each user-specific control for each assay.
6. Sample information is input, including appropriate dilution factors.
7. A software protocol adapted to the analyte of interest is run.
The data is obtained by the following steps:
1. the assay plate was shaken at 1,100rpm for 30sec and the plate was visually inspected to ensure that the assay wells were filled with buffer.
2. The protocol is run to begin acquiring data.
3. The "wash between plates" function is used after each plate run to reduce clogging.
Data analysis and outlier removal are then performed.
Outliers are defined as standard data points that do not meet accuracy and precision requirements and are considered invalid when curve fitting is performed. As such, they should be removed to produce a more realistic and accurate standard curve. This can lead to an extended assay working range and allow quantification of samples that might otherwise be considered out-of-range (OOR).
In Bio-Plex management software version 6.0, outliers can be automatically removed by selecting the optimization button in the standard curve window. In 6.0 and earlier versions of the Bio-Plex management software, outliers can also be manually selected in the report table.
Computing
Bio-PlexTMThe Pro instruction manual details the following calculations:
layout of planning board
1. The 96-well plate template (page 43) is populated as explained in the layout section of the layout (page 13).
These guidelines are followed if a pre-mixed plate or a singleplex assay is used.
Input number of wells to be used in assay: _______ (1)
Counting coupled beads
1. The volume of 1 × coupled beads needed was determined.
a. Each well required 50 μ Ι of coupled beads (1 ×): _______ (1) × 50 μ l ═ _______ μ l (2)
b. A 20% excess was included to ensure sufficient volume: _______ μ l (2) x 0.20 ═ _______ μ l (3)
c.1 × total volume of coupled beads: _______ μ l (2) + _______ μ l (3) ═ _______ μ l (4)
d.20 × volume of coupled bead stock solution: _______ μ l (4)/20 ═ _______ μ l (5)
e. Volume of assay buffer required: _______ ul (4) - _______ ul (5) ═ _______ (6)
Counting coupled beads
1. The volume of 1 × coupled beads needed was determined.
a. Each well required 50 μ Ι of coupled beads (1 ×): ______ (1) × 50 μ l ═ _______ μ l (2)
b. A 20% excess was included to ensure sufficient volume: _______ μ l (2) x 0.20 ═ _______ μ l (3)
c.1 × total volume of coupled beads: _______ μ l (2) + _______ μ l (3) ═ _______ μ l (4)
d.20 × volume of coupled bead stock solution: _______ μ l (4)/20 ═ _______ μ l (5)
e. Volume of assay buffer required: _______ ul (4) - _______ ul (5) ═ _______ (6)
Counting coupled beads
1. The volume of 1 × coupled beads needed was determined.
a. Each well required 50 μ Ι of coupled beads (1 ×): _______ (1) × 50 μ l ═ _______ μ l (2)
b. A 20% excess was included to ensure sufficient volume: _______ μ l (2) x 0.20 ═ _______ μ l (3)
c.1 × total volume of coupled beads: _______ μ l (2) + _______ μ l (3) ═ _______ μ l (4)
d.20 × volume of coupled bead stock solution: _______ μ l (4)/20 ═ _______ μ l (5)
e. Volume of assay buffer required: _______ ul (4) - _______ ul (5) ═ _______ (6)
If a single-plex assay is mixed, these guidelines are followed.
Counting coupled beads
1. The volume of 1 × coupled beads needed was determined.
a. Each well required 50 μ Ι of coupled beads (1 ×): _______ (1) × 50 μ l ═ _______ μ l (2)
b. A 20% excess was included to ensure sufficient volume: _______ μ l (2) x 0.20 ═ _______ μ l (3)
c.1 × total volume of coupled beads: _______ μ l (2) + _______ μ l (3) ═ _______ μ l (4)
d. Inputting the number of diabetes uniset (or analyte) tubes to be multiplexed _______ (5)
e. 20 x volume of coupled beads obtained from each tube of coupled beads: ___ μ l (4)/20 ═ ___ μ l (6)
f. Total volume of diabetic bead stock solution required: _______ (5) x _______ μ l (6) ═ _______ μ l (7)
g. Volume of assay buffer required: _______ μ l (4) - _______ μ l (7) ═ _______ μ l (8)
Calculating detection antibody
2. The required volume of 1 × detection antibody was determined.
a. Each well required 25 μ l of detection antibody (1 ×): _______ (1) × 25 μ l ═ _______ μ l (9)
b. A 25% excess was included to ensure sufficient volume: _______ μ l (9) x 0.25 ═ _______ μ l (10)
c.1 × total volume of detection antibody: _______ μ l (9) + _______ μ l (10) ═ _______ μ l (11)
d. Inputting the number of diabetes uniset (or analyte) tubes to be multiplexed _______ (5)
e. 20 x volume of detection antibody obtained from each detection antibody tube: ___ μ l (11)/20 ═ ____ μ l (12)
f. Total volume of diabetic test antibody stock solution: _______ μ l (12) x _____ (5) ═ _______ μ l (13)
g. Volume of detection antibody diluent required: _____ μ l (11) - _____ μ l (13) ═ ______ μ l (14)
Calculation of streptavidin-PE
3. The required volume of 1 × streptavidin-PE was determined.
a. 50 μ l of streptavidin-PE (1 ×) ______ (1) × 50 μ l ═ _______ μ l (15) was required for each well
b. A 25% excess was included to ensure sufficient volume: _______ μ l (15) x 0.25 ═ _______ μ l (16)
Total volume of 100 × streptavidin-PE ______ μ l (15) + ______ μ l (16) ═ ______ μ l (17)
d. The volume of 100 XSstreptavidine-PE required is _______. mu.l (17)/100. mu. _______. mu.l (18)
e. Volume of assay buffer required: _______ μ l (17) _______ μ l (18) ═ _______ μ l (19)
Data processing
All plasma samples from 50 glioma patients and 27 healthy subjects were assayed for various cytokines and angiogenic factors, and the levels of the cytokines and angiogenic factors were determined in each case. The mean values of the levels of cytokines and angiogenic factors for 50 glioma patients ("glioma mean") and 27 healthy subjects ("control mean") were generated for each cytokine and angiogenic factor assayed, respectively, and the results compared. Statistical comparisons were then made regarding the significance of particular cytokines and angiogenic factors correlated with their ability to indicate the presence of gliomas.
FIGS. 1 to 7 show graphical representations of "control mean" (light grey), "glioma mean" (dark grey) and error bars for IL-8, angiogenin, follistatin, HGF, leptin, PDGF-BB and PECAM-1, respectively.
FIGS. 7A to 7F show graphical representations of "control mean" (dark grey-left), "low grade glioma mean" (light grey-middle) and "high grade glioma mean (middle grey-right)" and error bars for FGF, G-CSF, sHER2neu, sIL-6R α, prolactin, and sVEGFR1, respectively. These figures illustrate the applicability of the invention to both low and high grade cancers.
FIG. 8 is a graph correlating the scatter patterns of PECAM-1 and PDGF-BB, showing the relationship between PECAM-1 and PDGF-BB levels in 50 glioma patients, and showing the degree of linearity and a correlation coefficient of 0.45. This suggests that a good correlation of favorable or unfavorable diagnostic results with respect to relative levels of both PECAM-1 and PDGF-BB may be provided in relation to gliomas. It is also reasonable to use the relative levels or ratios between other groups of cytokines and/or angiogenic factors as an indication of a favorable or unfavorable diagnostic result associated with gliomas, or even with other brain cancers.
Results
Table 15 below compares the "control mean" concentration of each measured cytokine and angiogenic factor to the "glioma mean" concentration of each measured cytokine and angiogenic factor and reports the "significance" of the particular cytokine or angiogenic factor investigated (i.e., whether the cytokine or angiogenic factor is a suitable biomarker in the plasma of a glioma).
Table 15-comparison of "control mean" and "glioma mean" to determine significance as a biomarker for glioma
As will be apparent, at least IFN- γ, angiogenin, follistatin, HGF, IL-8, leptin, PDGF-BB, PECAM-1, PDGF-AA, sHER2neu, sIL-6 Ra, prolactin, sVEGFR1, G-CSF, and FGF show a high degree of "significance", although the significance of IFN- γ is carefully handled in view of many individuals exhibiting zero concentration of this particular cytokine. Furthermore, higher levels of follistatin were observed in glioma patients than in healthy subjects, with higher interleukin 10, lower angiogenin, higher leptin, and higher PDGF-BB. It is therefore clear that these cytokines and angiogenic factors are excellent candidates as plasma biomarkers for gliomas, and it is reasonable to assume that many other cytokines and/or angiogenic factors may also have excellent biomarker characteristics in this regard. Furthermore, it is reasonable to conclude that other forms of brain cancer are also detectable by reference to cytokines and/or angiogenic factor biomarkers.
In view of the above disclosure, related diagnostic kits and methods can be readily developed using conventional plant techniques known in the art.
The above data was further confirmed by immunohistochemical comparison between glioma tissue and non-cancerous brain tissue. Figures 8A-8G show a photographic immunohistochemical comparison between glioma and non-cancerous brain tissue, namely: A) glioma sections showing 40-fold magnification of positively stained and unstained tumor cells; B) a glioma tumor section showing 40-fold magnification of negatively stained blood vessels; C) non-cancerous brain tissue at 40-fold magnification showing negatively stained blood vessels; D) a glioma tumor section showing 40-fold magnification of interstitial staining; E) glioma tumor sections showing 40-fold magnification of interstitial staining, particularly axonal bundle staining; F) non-cancerous brain tissue at 40-fold magnification showing negatively stained blood vessels; G) positively cytoplasmic stained choroid plexus tissue is shown.
In particular, fig. 8A-8G show immunohistochemical staining of follistatin, showing increased accumulation of this protein in brain tissue of glioma patients.
Figures 8A-8G show the ability of follistatin to identify tumor margins (tumours margin) during immunohistochemical staining of brain tissue. Some gliomas displayed significant follistatin immunostaining of tumor cells, many of which were shown to express a mast astrocytic morphology. However, staining was not uniform throughout the tumor sample and some cells were apparently immune negative (panel a). Positive immunostaining was all cytoplasmic with no cellular membrane or nuclear components and other tissue components in the sections, including blood vessels, were completely negative (panel B). There are no specific characteristics of the tumor or structural cells that are clearly predictive of immunopositivity or that indicate significant variability between individual tumors. Non-cancerous (i.e., normal) brain tissue is all consistently negative and there is no staining of neurons or glial cells (panel C). There was significant interstitial staining in the presence of negatively stained cells after axonal bundles of sections (panels D and E). There is no specific axonal staining and some axonal bundles do not absorb any staining agent. Non-cancerous brain axon bundles were consistently negative (panel F). There was some specific cytoplasmic staining of cells from the choroid plexus (panel G). This may indicate that follistatin is secreted into the CSF.
Example 2 spectroscopic analysis of blood samples
In this example, spectroscopic analysis was performed on serum samples using attenuated total reflectance fourier transform infrared spectroscopy (ATR-FTIR).
JASCO FTIR-410-Specac Golden GateTMThe spectrometer was used to perform spectroscopic experiments and the infrared spectra of serum samples were taken at 4cm using a total of 32 scans after us-1Resolution of 400-4000cm-1。
Whole blood sampling
Whole blood samples were collected from a total of 74 subjects, including 49 patients with grade IV glioblastoma and 25 healthy subjects. Where possible, samples were matched for age and gender.
Preparation of serum samples from Whole blood samples
Serum samples for each of the 74 subjects were prepared by allowing a fresh whole blood sample to first clot at room temperature (25 ℃) for 30 to 45 minutes, and then performing centrifugation in a refrigerated centrifuge at 13,200rpm (or 1000-2000x g) at 4 ℃ for 10 minutes to clear the precipitated sample. The obtained supernatant was serum. It is crucial that the serum is transferred to a clean polypropylene tube immediately after centrifugation by means of a pasteur pipette or similar. The samples were maintained at 2-8 ℃ at the time of processing and then divided into aliquots, stored and transported at-20 ℃ or lower. Freeze-thaw cycles are avoided.
In this example, serum samples related to the above-mentioned patients and healthy subjects were provided by BrainTumour North West Biobank. Four serum fractions were prepared and analyzed. The following fractions ("serotypes 1-4") were prepared:
1) whole serum-direct supply.
2) Serum from fractions above 100kDa was removed by centrifugal filtration.
3) Serum from fractions above 10kDa was removed by centrifugal filtration.
4) Serum from fractions above 3kDa was removed by centrifugal filtration.
Centrifugation was performed using a microcentrifuge in combination with a suitable protein filter (Amicon membrane filter, merck millipore) at 14,000rpm as per the manufacturer's instructions.
Sample loading onto ATR
For each of serum samples 1-4, 1 μ Ι _ of serum was placed on the element of the ATR-FTIR accessory (i.e., the ATR crystal) and left to dry for 8 minutes at room temperature. This has been shown to be reproducible drying times for 1 microliter of serum.
Fig. 9 shows a white light interference spectrum of a film of serum sample 1 (i.e., whole serum) deposited and dried according to the above protocol. The thickness across the ATR crystal fluctuates between 0 and 40 microns thickness. This was found to be an ideal thickness for ATR-FTIR analysis of whole serum.
Figure 10 shows a white light interference pattern of the membrane of serum sample 3 (serum with components above 10kDa removed) deposited and dried according to the above protocol. The thickness across the ATR crystal fluctuates between 0 and 8 microns thickness. This was found to be an ideal thickness for ATR-FTIR analysis of whole serum.
The sample preparation procedure described above and the analysis described subsequently were performed twice for each serum sample in order to verify the results.
Various additional side experiments (side experiments) were performed at different drying times to investigate the effectiveness of drying times on film thickness and the indirect effect on the resulting IR spectral characteristics obtained (results are discussed in more detail below).
ATR-FTIR spectroscopy of prepared serum samples
The sample-loaded ATR crystals were then removed by JASCO FTIR-410-Specac Golden GateTMThe spectrometer was used to analyze to provide a serum sample at 4cm for each subject-1Resolution Using 32 total additional scans at 400-4000cm-1A series of spectral features in between. The IR spectral runs were repeated three times to generate 3 times per subject for a total of 222 features.
The spectral features were then clipped to 1800 and 900 wavenumbers (cm)-1) Fingerprint area in between and vector normalized.
FIG. 11 shows a representative sample of superimposed FTIR spectral features for each of serum sample types 1-4. Characteristic fingerprint region (at 900 and 1800 cm)-1In between) appears most obvious, indicating a highly relevant amount of information in this particular area.
Various additional side experiments were performed, running ATR-FTIR spectra of various samples to elucidate the feasibility of ATR-FTIR and the effect of various parameters on the resulting spectral characteristics.
ATR-FTIR side experiments showing the effect of film thickness on certain IR peaks
ATR-FTIR spectroscopy performed on Bovine Serum Albumin (BSA) has been reported in the literature. Since BSA contains some key components that are also contained in serum, the literature IR signatures are considered and compared. Furthermore, literature features were considered from BSA and serum experiments performed by the inventors.
Fig. 12 (taken from Filik J, frog MD, et al, Analyst,2012, 137, 853) shows superimposed FTIR spectral features of samples of Bovine Serum Albumin (BSA) at different average membrane thicknesses on ATR crystals. 900-1800cm-1There is a clear variation in film thickness in the spectral features in the fingerprint region.
FIG. 13 (taken from Goormightigh E, et al, Biochimica et Biophysica Acta,1999, 1422, 105) is a graph showing a) the presence of two characteristic amides, amide I, in serum samples(1650cm-1) And amide II (1550 cm)-1) How the area ratio of (a) varies with the BSA film thickness and b) amide I (1650 cm)-1) And TSPA internal Standard (835 cm)-1) Is a graphical representation of how the area ratio of (a) varies with BSA film thickness. These amides are believed to be important in diagnosing glioma from serum, as changes in these peaks appear to indicate structural changes in the protein associated with the presence of glioma or with glioma in the subject. However, fig. 13 demonstrates that sample thickness can also affect such peaks. The present inventors therefore believe that it is desirable to eliminate or address the effects of sample thickness variations to improve the utility of spectroscopy in the diagnosis of proliferative disorders such as gliomas.
The inventors found that the ratio of amides I and II remained substantially constant between BSA samples when the samples were prepared on ATR plates at a thickness of 0.8 microns. The inventors have further demonstrated that when samples are prepared on ATR plates at a thickness of 0-40 microns (as described above), the ratio of amides I and II remains substantially constant between whole serum samples taken from subjects in the same category (i.e., both healthy subjects or both with glioma). Furthermore, the inventors have demonstrated that when samples were prepared on ATR plates at a thickness of 2-8 microns (as described above), the ratio of amides I and II remained substantially constant between serum sample types 3 (i.e. sera with over 10kDa components removed) taken from subjects in the same category (i.e. both healthy subjects or both with gliomas).
Thus, these results show that ATR-FTIR spectroscopy on serum samples is a viable method to discriminate glioma patients from healthy subjects, although optimizing sample preparation is crucial to optimizing results. The sample thickness on the ATR plate showed a particular correlation to the diagnostic quality of the feature.
ATR-FTIR side experiments demonstrating the effect of sample drying time on IR characteristics
Several ATR-FTIR spectroscopic analyses were performed on the same human whole serum with different dryness levels on the ATR crystals prior to analysis. After application to the ATR crystal, serum samples were dried on the surface of the ATR crystal at room temperature (25 ℃) for 0, 2, 4, 6, 8, 16 and 32 minutes as described above before performing the spectral analysis.
Fig. 14 shows various superimposed spectral signatures of human whole serum dried at room temperature for 0, 2, 4, 6, 8, 16 and 32 minutes. The drier the membrane is, at 900--1The more information that is present is shown by the fingerprint area in between, and thus the more appropriate the feature is for diagnostic analysis relating to glioma. However, there is a trade-off for long drying times as the present inventors have attempted to provide diagnostic tools for rapid diagnosis of proliferative disorders by spectroscopy. In this way, a drying time of between 6 and 12 minutes, preferably about 8 minutes, will appear to be optimal, as drying times of more than 8 minutes obtain little additional information in the fingerprint area.
Post-processing of spectral features of serum samples
Uploading all spectral features and corresponding actual information (e.g. medical condition, gender, age, etc.) relating to glioma patients and healthy subjects to a database, e.g. MATLABTMSo that they can be recalled, tested, statistically analyzed, or even used as a comparison data set for testing features that have not been correlated.
The 74 spectral features (x 3) obtained for serotype 1 samples (full serum samples of 74 subjects, all dried according to the optimized 8 minute protocol described above) were divided into a "training set" (two thirds) and a "blind set" (one third):
33 trained, 16 blind, for the features of the whole serum samples taken from 49 patients with grade IV glioblastoma; and is
For the characteristics of the whole serum samples taken from 25 healthy subjects, 17 trained, 8 blind.
The training set is then used to build a predictive model using two different pattern recognition algorithms:
1) support Vector Machines (SVMs) -e.g., RBFs, see Baker et al, analysis 2010, 135(5), satlectker et al, analysis 2010, 135 (5); and
2) principal component discriminant function analysis (PC-DFA).
After being separated into a "training set" of features and a "blind set" of features, the features can be used to develop a powerful predictive model that can assign favorable or unfavorable diagnostic outcomes to unassigned features. The "training set" is trained using a pattern recognition algorithm by performing a grid search to optimize the cost and gamma function to ensure that it can identify the training set, thereby producing a viable predictive model. The "blind set" is then provided to a model, which is then requested to predict whether individual features in the blind set should be associated with favorable or unfavorable diagnostic and/or prognostic outcomes. The predictions can then be translated into a "confusion matrix" that sets forth which predictions were made. These predictions can then be validated (e.g., by validating actual results, e.g., from a biopsy) to calculate the sensitivity and specificity of the model.
Fig. 15 is a graph illustrating the training set accuracy of the whole serum predictive model when the predictive model is used to evaluate the "blind set". When using the predictive model generated from the training set to assign diagnostic results to the "blind set" features, 21 of the 216 spectra (i.e., where 3 replicate spectra were all used) were misclassified, giving 88.19% sensitivity and 94.44% specificity.
The same training was performed on 74 spectral features (× 3) obtained for a serotype 3 (molecular weight components above 10kDa removed) sample, which was still all dried according to the optimized 8 minute protocol described above. These were again divided into a "training set" (two thirds) and a "blind set" (one third) as described above for the serum type 1 samples.
Fig. 16 is a graph illustrating the training set accuracy of the serotype 3 predictive model when evaluating the relevant "blind set" against the predictive model. When the diagnostic results were assigned to the "blind set" features using the predictive model generated from the training set, 38 of the 216 spectra (i.e., where 3 replicate spectra were all used) were misclassified, giving a sensitivity of 78.9% and a specificity of 88.9%.
Thus, serotype 1 (whole serum) appears to produce a better predictive model than serotype 3 in terms of overall sensitivity and specificity. This simplifies the diagnostic method of the invention even further, since no further processing of the whole serum is required in order to obtain reliable diagnostic results.
These results demonstrate excellent diagnostic potential for the spectral signatures associated with proliferative disorders such as gliomas. Clearly, the described predictive model can be further improved by training a larger feature data corpus. A more improved database may additionally contain additional real information about the investigated subject, which may enable more patient-specific predictions. The feature database can be readily used alone or in combination with predictive models to correlate unassigned features with favorable or unfavorable diagnostic outcomes, such as by "best match" comparison.
Predictive models such as those described herein can be readily incorporated into computer software installed on an on-board computer (on-board computer) of a diagnostic kit in order to provide a simple diagnostic kit capable of performing rapid diagnosis. Such diagnostic kits may be incorporated into or otherwise capable of communicating with a spectroscopic device (or its associated feature storage unit) so that the predictive algorithm can be run on the obtained serum feature. Due to the contribution made by the present invention to the art, it is now easy to think of how to create a simple, cost-effective diagnostic kit that allows a rapid diagnosis of proliferative disorders from blood samples only. Using techniques known in the art, a diagnostic kit can be readily adapted to include a series of functions to automate any or all of the method steps described herein.
It is also easy to imagine how the spectroscopic diagnostic kit described herein can be used in conjunction with an assay kit to provide highly accurate, reliable and well-validated diagnostic results without the need for invasive biopsy or costly imaging. Alternatively, the diagnostic methods and kits described herein can be used for prescreening before expensive and/or invasive diagnostic methods are used.
Example 2A-spectroscopic analysis of blood samples
Method and material
Serum sample
Blood sample collection 49 patients diagnosed with glioblastoma multiforme (GBM) brain tumors (i.e., high grade), 23 patients diagnosed with low grade gliomas (astrocytomas, oligodendroastrocytomas, oligodendrogliomas), and 25 normal (non-cancerous) patients were diagnosed. Samples were obtained from Walton Research Tissue Bank and Brain Tumour NorthWest (BTNW) Tissue Bank, where all patients had given the consent. The details of the blood samples are summarized in table 16 below.
All blood samples were taken prior to the procedure. From blood thawing to centrifugation, the serum tube is left to clot at room temperature for a minimum of 30 minutes and a maximum of 2 hours. Separation of the clot was accomplished by centrifugation at 1,200g for 10min and a 500 μ l aliquot of serum was dispensed into a pre-labeled cryovial. Serum samples were flash frozen using liquid nitrogen and stored at-80 ℃.
The average age of the entire sample set was 54.62 years. Age and sex of GBM and control serum samples were matched, if possible.
TABLE 16 blood sample details
| Tumor grade | Number of subjects | Age range/mean age | Sex |
| Normal (non-cancer) | 25 | 26-87/59.1 year old | 29 males and 20 females |
| Low level | 23 | 19-60.3/36.9 years old | 11 men and 12 women |
| High grade | 49 | 24.7-78.8/60.1 year of |
15 men and 10 women |
Drying study
Pooled normal human serum (0.2 μ L sterile filtered, CS100-100, available from TCSBiosciences, UK) was used in a volume of 1 μ L to determine the optimal drying time necessary for mass spectral collection.
Use of a single reflective diamond Golden Gate equipped with Specac ATRTMJASCO FTIR-410 spectrometer at 4000-400cm, university of orchestra, central office-1In the range of 4cm-1And spectra were collected over 32 total scans. Before each spectrum collection, a background absorption spectrum was collected for atmospheric correction.
mu.L of serum was pipetted onto ATR-FTIR crystals and spectra were collected at 0, 2, 4, 8, 16 and 32 minute intervals to observe spectral changes during drying. The dried compact serum film was washed off the crystals using absolute ethanol (purchased from Fisher Scientific, Loughborough, UK). One biological replicate and two technical replicates were collected for each 1 μ L of dried serum. The drying experiment was repeated multiple times to obtain a representative spectrum at a specific time during drying.
Variance study
Pooled normal human serum (0.2 μ L sterile filtered, CS100-100, available from TCSBiosciences, UK) was used for variance studies, where 1 μ L was pipetted onto ATR-FTIR single-reflecting diamond crystals and dried for 8 minutes, at which time 3 spectra were collected. Three spectra were collected every 1 μ L and repeated 50 times. Dried serum plaques were washed from the crystals with absolute ethanol (purchased from Fisher Scientific, Loughborough, UK) between each replicate of the variances. In total, 150 ATR-FTIR spectra were collected from the variance study.
ATR-FTIR spectrum diagnosis model
Prior to collection of spectra, all whole serum samples were thawed and 100kDa, 10kDa and 3kDa filtration aliquots were prepared using Amicon Ultra-0.5 mL centrifugal filters (purchased from Millipore Limited, UK) [ figure 1 ]. The centrifugal filter filters out serum components above the cut-off point (i.e., 100kDa) of the filter membrane, allowing components below the cut-off point of the filter membrane to pass through.
Figure 17 shows that 0.5ml of serum is pipetted into the centrifugal filter (left) and centrifuged such that the filter retains all serum components above the kilodalton range (100, 10 or 3kDa), allowing only serum filtrates containing components below the maximum range to pass.
Each whole serum sample (high, low and control) had a filtration aliquot prepared by pipetting 0.5mL of whole serum into a filtration apparatus and centrifuging at 14,000rpm for 10 minutes, 15 minutes and 30 minutes for 100kDa, 10kDa and 3kDa filtration devices, respectively.
Spectra were collected in a random order in a serum sample set. For each sample, 1 μ Ι _ of serum spot was dried on ATR-FTIR crystals for 8 minutes, at which time 3 spectra were collected. This procedure was repeated three times for each sample. As a result, 9 spectra were collected for each sample. Background absorption spectra (for atmospheric correction) were collected before pipetting 1 μ Ι _ onto ATR-FTIR crystals prior to spectrum collection, thus collecting the background for each serum replicate sample. In use
Between each procedure of disinfectant (available from Antec int., Suffolk, UK) and absolute ethanol (available from Fisher Scientific, Loughborough, UK), dried serum films were washed off the crystals.
The spectrum is 4000-400cm-1In the range of 4cm-1Is more than 32 times of total additionIs obtained by scanning. In total, 3375 ATR-FTIR spectra were collected from all whole serum samples and filtered serum samples. Table 2 shows the spectra and total number of patients in each serum grade and filtration category.
Table 17-collected light for each of the filtered compositions for the range of cancer serum severity analyzed
Number of spectra and number of patients (in parentheses)
Data analysis
Matlab pair using internal writing softwareTM(7.11.0(R2010b) (The MathWorks, Inc. USA) in The raw spectral data implementation of pretreatment and Multivariate (MVA) analysis.
Results
Drying study
Fig. 18 shows various superimposed ATR-FTIR spectral features of human whole serum dried at room temperature for 0, 2, 4, 6, 8, 16 and 32 minutes. The spectra have been biased for easy visualization.
Fig. 18 shows ATR-FTIR crystal data commonly observed from 1 μ L of human whole serum over the range of 0-32 minutes during drying. The spectra have been biased for easy visualization. By repeating the drying experiment, 1 μ L of serum at room temperature (. about.18 ℃) was found to dry after 8 minutes. Efficient spectral collection requires close contact between the serum sample and the ATR-FTIR crystal to allow interaction with the evanescent field; this can be achieved by allowing the liquid serum sample to dry. Drying allows the intensity of the band to increase exponentially as the expansion decreases, thus reducing the distance between the reflection interference (water) and the sample molecules.
Variance study
FIGS. 19A-D show (A) the whole serum spectrum (900--1) And (B) fingerprint region (900--1) (C) pre-processed whole serum spectra and (D) raw and unprocessed spectral data of the pre-processed fingerprint region. VariableCO2Zone (2300--1) Has been removed. The four spectra show the average spectrum surrounded by the standard deviation (STD) error range. The maximum variance between the raw (unprocessed) spectral data in the wavenumber region of the two analyses was 1637.27cm-1(STD: 0.4209). At 3900-900cm-1In between, the minimum variance in the raw data is at 3735.44cm-1(STD: 0.0038) and in the fingerprint area is 1792.51cm-1(STD: 0.0138). Noise reduction (30 principal components) and vector normalization preprocessing methods were applied to the data to reduce the baseline and smooth the data. The preprocessing method significantly reduces the STD and variance of the spectral data. At 1637.27cm-1The maximum raw data STD at (d) was reduced from 0.4209 to 0.0043 (preprocessed), a difference of 195.9%. At 3735.44cm-1The minimum spectral variance STD at (A) is reduced from 0.0038 to 0.00123 and at 1792.51cm-1From 0.0138 to 0.0004. Cross 3900-900 cm--1The average STDs of the raw and preprocessed data of (a) are 0.0137 and 0.0015, respectively. The STD value of the original spectrum is initially low but is further reduced by implementing a pre-processing method. The reproducibility of the spectral data using ATR-FTIR is high and exhibits minimal variance, especially after pre-processing.
Preprocessing selection data
For each of the whole serum sample set and the filtered serum sample set, the spectral data was pre-processed using the same method and analyzed using multivariate analysis. First, to remove any bias from the analytical model, the technique from each sample was repeatedly averaged such that each serum sample set contained three spectra from each patient; one average spectrum from each patient spot. Outliers are then removed from the spectral set using a quality check to discern anomalous spectral data. In this case, the quality controlled spectrum typically corresponds to a particular patient.
Principal component-based noise reduction is performed on the spectra using the first 30 principal components of the data to improve the signal-to-noise ratio. After this, all spectral vectors are normalized and mean centered. The spectral data was also analyzed using second derivative spectra of the data, but the best overall results for PCA and SVM were achieved using noise reduction, vector normalization and mean centering procedures.
Principal Component Analysis (PCA) is performed on the preprocessed spectra, giving an unsupervised classification by which the load can be interpreted. A Support Vector Machine (SVM) is also applied to the data set using a Radial Based Function (RBF) kernel. Using the LIBSVM code in MATLAB (Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector technologies. ACM Transactions on Intelligent Systems and technologies, 2:27:1-27:27,2011. software in MATLABhttp://www.csie.ntu.edu.tw/～cjlin/libsvmThe above is available), automatic n-fold cross validation is performed on the data to find the best values for the cost and gamma functions. These values are then used to train the SVM in a pair of multiple modes using a randomly selected training set consisting of two-thirds of the patient-related spectral data. The remaining data that make up the blind test set is then put into the model and a confusion matrix is calculated giving the overall SVM classification accuracy based on the true and predicted data class labels. The sensitivity and specificity for each SVM model and each independent disease group were calculated.
The results shown in tables 18, 19 and 20 below are derived from three different tests and blind spectral sets to provide a range of sensitivity and specificity for whole serum.
TABLE 18 statistical analysis of test 1 on whole serum
Best SVM: 22.63 percent of C, 4 percent of gamma, 85.86 percent of training accuracy and 96.875 percent of total accuracy of SVM
TABLE 19 statistical analysis of test 2 on whole serum
| 2 | Is normal | Is low in | Height of | Total mean |
| Patient sensitivity | ||||
| 75 | 87.50 | 92.86 | 85.12 | |
| Patient specificity | 95.45 | 95.45 | 87.5 | 92.80 |
| Spectral sensitivity | 78.26 | 91.67 | 92.86 | 87.60 |
| Spectral specificity | 95.45 | 96.88 | 89.13 | 93.82 |
TABLE 20 statistical analysis of test 3 on whole serum
| 3 | Is normal | Is low in | Height of | Total mean value |
| Patient sensitivity | 87.5 | 87.5 | 93.33 | 89.44 |
| |
100 | 95.65 | 87.5 | 94.38 |
| Spectral sensitivity | 87.5 | 85 | 93.18 | 88.56 |
| |
100 | 95.45 | 86.36 | 93.94 |
The results shown in table 21 below are derived from corresponding tests on 100kDa filtered sera.
Watch (A)21-statistical analysis for testing of 100kDa filtered serum
Best SVM: 2048% of C, 0.85% of gamma, 72.58% of training accuracy, and 79.57% of total SVM accuracy
| Is normal | Is low in | Height of | Total mean | |
| Patient sensitivity | ||||
| 50 | 57.14 | 100 | 69.05 | |
| Patient specificity | 95.45 | 95.45 | 66.7 | 85.87 |
| Spectral sensitivity | 54.17 | 61.90 | 93.75 | 69.94 |
| Spectral specificity | 94.12 | 94.12 | 67.44 | 85.39 |
Abbreviations
ATR-attenuated Total reflection
Basic FGF-Basic fibroblast growth factor
beta-NGF-nerve growth factor-beta
CTACK-cutaneous T-cell-attracting chemokines
FTIR-Fourier transform Infrared
G-CSF-granulocyte colony stimulating factor
GM-CSF-granulocyte-macrophage colony stimulating factor
GRO-growth-related oncogenes
GRO-alpha-growth-related oncogene alpha
HGF-hematopoietic growth factor
ICAM-1-intercellular adhesion molecule 1
IFN-gamma-interferon gamma
IGFBP-1-insulinlike growth factor binding protein 1
IL-1 alpha-interleukin-1 alpha
IL-1 beta-interleukin-1 beta
IL-1 ra-interleukin 1 receptor antagonists
IL-1R 1-interleukin 1 receptor associated protein 1
IL-1R4/ST 2-Interleukin 1 receptor 4, ST2
IL-2-interleukin-2
sIL-2R alpha-interleukin 2 soluble receptor alpha
IL-3-Interleukin-3
IL-4-Interleukin-4
IL-5-Interleukin-5
IL-6-Interleukin 6
IL-6R-interleukin 6 receptor
IL-7-Interleukin 7
IL-8-Interleukin 8
IL-10-Interleukin 10
IL-11-Interleukin 11
IL-12p 40-Interleukin 12p40
IL-12p 70-Interleukin 12p70
IL-13-Interleukin 13
IL-15-Interleukin 15
IL-16-Interleukin 16
IL-17-Interleukin 17
IL-18-interleukin 18
IR-Infrared
MCP-1-monocyte chemotactic protein 1
MCP-3-monocyte chemotactic protein 3
M-CSF-macrophage colony stimulating factor
MIF-macrophage migration inhibitory factor
MIG-gamma interferon-induced monokines
MIP-1 alpha-macrophage inflammatory protein 1 alpha
MIP-1 beta-macrophage inflammatory protein 1 beta
MIP-1-macrophage inflammatory protein 1
MIP-3 alpha-macrophage inflammatory protein 3 alpha
MIP-3 beta-macrophage inflammatory protein 3 beta
MSP-alpha-macrophage stimulating protein alpha chain
PAI-1-plasminogen activator inhibitor 1
PDGF AA-platelet derived growth factor AA
PDGF-BB-platelet derived growth factor BB
PlGF-placental growth factor
RANTES-Normal T cell expression factor Regulated by activation (Regulated UP activation, normal T-cell expressed)
SCF-stem cell factor
SDF-1-stromal cell derived factor
sgp 130-soluble glycoprotein 130
sHER2 neu-human epidermal growth factor receptor 2
sIL-6R alpha-soluble interleukin 6 receptor alpha
sTNF RI-soluble TNF receptor I
sTNF RII-soluble TNF receptor II
sVEGFR 1-soluble vascular endothelial growth factor receptor 1
TARC-Thymus activation-regulated chemokines
TECK-Thymus gland expression chemotactic factor
TGF-beta 1-tumor necrosis factor beta 1
TGF-beta 3-tumor necrosis factor beta 3
TIMP-1-MORPHOLIN TISSUE INHIBITOR 1
TIMP-2-METALLURGETIN TISSUE INHIBITOR 2
TNF-alpha-tumor necrosis factor-alpha
TNF-beta-tumor necrosis factor-beta
TPO-thrombopoietin
TRAIL R3-TNF-related apoptosis-inducing ligand receptor 3
TRAIL R4-TNF-related apoptosis-inducing ligand receptor 4
VEGF-vascular endothelial growth factor
VEGF C-vascular endothelial growth factor C
VEGF-D-vascular endothelial growth factor D
Various alternative embodiments
The following numbered paragraphs cite certain alternative aspects and embodiments of the invention:
1. a method of diagnosing and/or prognosing a proliferative disorder in a subject, the method comprising performing spectroscopic analysis on a blood sample (or component thereof) of the subject to generate a spectroscopic signature characteristic of the blood sample (or component thereof); wherein the spectral analysis is attenuated total reflectance FTIR (ATR-FTIR), wherein an "ATR crystal" supports the blood sample during IR analysis.
2. The method of paragraph 1, wherein the proliferative disorder diagnosed and/or predicted is a brain cancer (and/or related tumor).
3. The method of paragraph 2, wherein said brain cancer is glioma.
4. The method of any of paragraphs 1 to 3, wherein a film of the blood sample is applied to the surface of the ATR crystal prior to FTIR analysis.
5. The method of paragraph 4, comprising depositing 0.5-1.5 μ L of said blood sample on the surface of said ATR crystal and allowing said blood sample to dry to obtain a blood sample film of appropriate thickness.
6. The method of paragraph 4 wherein drying is effected at Standard Ambient Temperature and Pressure (SATP) for between 4 and 16 minutes, or other equivalent conditions that result in the same level of drying.
7. The method of any of paragraphs 4 to 6, wherein the blood sample membrane has a substantially uniform thickness within a tolerance of +/-40 μm or less.
8. The method of any of paragraphs 4 to 7, wherein the blood sample film has a maximum film thickness (i.e. point of maximum thickness) between 1 μ ι η and 200 μ ι η across the surface of the ATR crystal (or at least the portion thereof exposed to IR analysis).
9. The method of any preceding paragraph, wherein the spectral feature (i.e., characteristic region) characteristic of the blood sample is at 900cm-1And 1800cm-1The spectrum of (a) and (b).
10. A method as claimed in any preceding paragraph, wherein the spectrally obtained features are compared with a plurality of pre-correlated features stored in a database (e.g. a "training set") in order to derive a correlation with a favorable or unfavorable diagnostic and/or prognostic outcome.
11. A method as described in any of the preceding paragraphs, wherein the spectrally obtained features are correlated with favorable or unfavorable diagnostic and/or predictive outcomes based on a predictive model developed through a database of pre-correlated analyses that are "trained" (e.g., via a pattern recognition algorithm).
12. The method of any preceding paragraph, wherein the blood sample is serum or plasma.
13. The method of paragraph 12 wherein the blood sample is serum.
14. The method of paragraph 13 wherein said serum is human whole serum.
15. The method of any preceding paragraph, wherein the method further comprises assaying the subject's blood sample (or a component thereof) for one or more (suitably pre-specified) cytokines and/or angiogenic factors.
16. A method of diagnosing whether a tumour is malignant or benign, the method comprising the steps of a method of diagnosing and/or prognosing a brain cancer or proliferative disorder according to any of paragraphs 1 to 15.
17. A diagnostic kit for diagnosing and/or prognosing a proliferative disorder in a subject, the kit comprising a device configured to receive a blood sample (or component thereof) from the subject and perform a spectroscopic analysis of the blood sample (or component thereof) of the subject to produce a spectroscopic signature characteristic of the blood sample (or component thereof); and a means (optionally the same as the means configured to receive a blood sample) to correlate or facilitate correlation of the spectral signature of the blood sample (or component thereof) with a favorable or unfavorable diagnostic and/or prognostic outcome; wherein the spectral analysis is attenuated total reflectance FTIR (ATR-FTIR), wherein an "ATR crystal" supports the blood sample during IR analysis.
18. The diagnostic kit of paragraph 17, wherein said means for analyzing said blood sample is the same as said means for correlating or contributing to said results.
19. The diagnostic kit of any of paragraphs 17 to 18, wherein the correlating device comprises or is in communication with a computer having installed thereon diagnostic computer software configured to operate the computer to perform predictive diagnosis and/or prognosis related to a proliferative disorder based on the spectral characteristics of a blood sample of a subject.
20. The diagnostic kit of any of paragraphs 17 to 19, wherein the device configured to receive a blood sample is configured to automatically prepare a blood sample (or components thereof) of a desired thickness and dryness.
Claims (36)
1. Use of a device configured to receive 0.1-10 μ Ι _ of a serum or plasma sample of a subject and perform spectroscopic analysis on the serum or plasma sample of the subject to produce a spectral signature characteristic of the serum or plasma sample in the manufacture of a kit for diagnosing and/or prognosing a proliferative disorder in a subject; wherein the spectral analysis is attenuated total reflectance FTIR (ATR-FTIR), wherein during IR analysis ATR crystal supports the serum or plasma sample and the serum or plasma sample is applied to the surface of the ATR crystal prior to FTIR analysis and allowed to dry to produce a serum or plasma sample film of suitable thickness.
2. The use of claim 1, wherein the proliferative disorder diagnosed and/or predicted is brain cancer.
3. The use of claim 2, wherein the brain cancer is glioma.
4. The use of claim 1, wherein 0.5-1.5 μ L of said serum sample or said plasma sample is deposited on the surface of the ATR crystal.
5. Use as claimed in claim 1, wherein drying is effected at standard ambient temperature and pressure for between 4 and 16 minutes or under other equivalent conditions yielding the same level of drying.
6. The use of any one of claims 1 to 5, wherein the serum sample membrane or the plasma sample membrane has a substantially uniform thickness within a tolerance of +/-40 μm or less.
7. The use of any one of claims 1 to 5, wherein the serum sample film or the plasma sample film has a maximum film thickness between 1 and 200 μm across the surface of the ATR crystal, or at least the portion thereof exposed to IR analysis.
8. The use of claim 6, wherein the serum sample membrane or the plasma sample membrane has a maximum membrane thickness between 1 and 200 μm across the surface of the ATR crystal or at least the portion thereof exposed to IR analysis.
9. The use according to any one of claims 1 to 5 and 8, wherein the spectral characteristics characteristic of the serum sample or the plasma sample are between 900 and 1800cm-1The spectrum of (a) and (b).
10. The use according to claim 6, wherein the spectral characteristics characteristic of the serum sample or the plasma sample are between 900 and 1800cm-1The spectrum of (a) and (b).
11. The use according to claim 7, wherein the spectral characteristics characteristic of the serum sample or the plasma sample are between 900 and 1800cm-1The spectrum of (a) and (b).
12. Use according to any one of claims 1 to 5, 8 and 10 to 11, wherein the spectrally obtained features are compared with a plurality of pre-correlated features stored in a database in order to derive a correlation with a favorable or unfavorable diagnostic and/or prognostic outcome.
13. Use according to claim 6, wherein the spectrally obtained features are compared with a plurality of pre-correlated features stored in a database in order to derive a correlation with a favorable or unfavorable diagnostic and/or prognostic outcome.
14. Use according to claim 7, wherein the spectrally obtained features are compared with a plurality of pre-correlated features stored in a database in order to derive a correlation with a favorable or unfavorable diagnostic and/or prognostic outcome.
15. Use according to claim 9, wherein the spectrally obtained features are compared with a plurality of pre-correlated features stored in a database in order to derive a correlation with a favorable or unfavorable diagnostic and/or prognostic outcome.
16. The use of claim 12, wherein the database is a training set.
17. The use of any one of claims 13-15, wherein the database is a training set.
18. The use of any one of claims 1-5, 8, 10-11, and 13-16, wherein the spectrally obtained features are correlated with favorable or unfavorable diagnostic and/or predictive outcomes based on a predictive model developed by training a database of pre-correlated analytes.
19. Use according to claim 6, wherein the spectrally obtained features are associated with favorable or unfavorable diagnostic and/or prognostic outcomes based on a predictive model developed by training a database of pre-associated analytes.
20. Use according to claim 7, wherein the spectrally obtained features are associated with favorable or unfavorable diagnostic and/or prognostic outcomes based on a predictive model developed by training a database of pre-associated analytes.
21. Use according to claim 9, wherein the spectrally obtained features are associated with favorable or unfavorable diagnostic and/or prognostic outcomes based on a predictive model developed by training a database of pre-associated analytes.
22. Use according to claim 12, wherein the spectrally obtained features are associated with favorable or unfavorable diagnostic and/or prognostic outcomes based on a predictive model developed by training a database of pre-associated analytes.
23. Use according to claim 17, wherein the spectrally obtained features are associated with favorable or unfavorable diagnostic and/or prognostic outcomes based on a predictive model developed by training a database of pre-associated analytes.
24. The use of claim 18, wherein the training is performed by a pattern recognition algorithm.
25. The use of any one of claims 19-23, wherein the training is performed by a pattern recognition algorithm.
26. The use of claim 1, wherein the serum sample is human whole serum.
27. The use of any one of claims 1-5, 8, 10-11, 13-16, 19-24, and 26, wherein the subject's serum or plasma sample is assayed for one or more cytokines and/or angiogenic factors.
28. The use of claim 6, wherein the serum or plasma sample of the subject is assayed for one or more cytokines and/or angiogenic factors.
29. The use of claim 7, wherein the serum or plasma sample of the subject is assayed for one or more cytokines and/or angiogenic factors.
30. The use of claim 9, wherein the serum or plasma sample of the subject is assayed for one or more cytokines and/or angiogenic factors.
31. The use of claim 12, wherein the serum or plasma sample of the subject is assayed for one or more cytokines and/or angiogenic factors.
32. The use of claim 17, wherein the subject's serum or plasma sample is assayed for one or more cytokines and/or angiogenic factors.
33. The use of claim 18, wherein the subject's serum or plasma sample is assayed for one or more cytokines and/or angiogenic factors.
34. The use of claim 25, wherein the subject's serum or plasma sample is assayed for one or more cytokines and/or angiogenic factors.
35. The use of claim 27, wherein the one or more cytokines and/or angiogenic factors are suitably pre-specified.
36. The use of any one of claims 28-34, wherein the one or more cytokines and/or angiogenic factors are suitably pre-specified.
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| GB201611057D0 (en) * | 2016-06-24 | 2016-08-10 | Univ Strathclyde | Spectroscopic analysis |
| AU2018244652B2 (en) | 2017-03-31 | 2023-02-02 | Dxcover Limited | Infra-red spectroscopy system |
| GB201809403D0 (en) * | 2018-06-07 | 2018-07-25 | Univ Strathclyde | Method |
| US11280732B2 (en) | 2018-08-20 | 2022-03-22 | Georgia State University Research Foundation, Inc. | Detection of melanoma and lymphoma by ATR-FTIR spectroscopy |
| CN109884297B (en) * | 2019-03-07 | 2023-02-03 | 中国科学院上海巴斯德研究所 | Clinical markers for assessing susceptibility to ARTI in children |
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| CN114216851A (en) * | 2020-11-27 | 2022-03-22 | 四川大学华西医院 | Acute pancreatitis assessment device based on surface enhanced Raman spectroscopy |
| WO2022238407A1 (en) * | 2021-05-12 | 2022-11-17 | Debdulal Roy | Cancer diagnostic |
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| US20160252510A1 (en) | 2016-09-01 |
| CN104981695A (en) | 2015-10-14 |
| US20170285030A1 (en) | 2017-10-05 |
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| US10288615B2 (en) | 2019-05-14 |
| CN104981695B (en) | 2018-09-11 |
| US20150301017A1 (en) | 2015-10-22 |
| CA2891370C (en) | 2024-01-02 |
| CA3050424C (en) | 2024-02-13 |
| GB201220573D0 (en) | 2013-01-02 |
| CN108956968A (en) | 2018-12-07 |
| WO2014076480A1 (en) | 2014-05-22 |
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